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Geoscience and Remote Sensing, IEEE Transactions on
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  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0196-2892
Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Geoscience and Remote Sensing publication information
    • PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • IEEE Transactions on Geoscience and Remote Sensing information for authors
    • PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Enhanced-Resolution SMAP Brightness Temperature Image Products
    • Authors: David G. Long;Mary J. Brodzik;Molly A. Hardman;
      Pages: 4151 - 4163
      Abstract: The NASA-sponsored Calibrated Passive Micro- wave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record Project team has generated a multisensor, multidecadal time series of high-resolution radiometer products designed to support climate studies. This project uses image reconstruction techniques to generate conventional and enhanced-resolution daily brightness temperature images on a standard set of map projections. Sensors included in CETB are the Aqua Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Scanning Multichannel Microwave Radiometer, and all Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder radiometers. These span frequencies between 6 and 89 GHz. This paper considers the issues of adding the L-band (1.6 GHz) Soil Moisture Active Passive (SMAP) radiometer measurements to the CETB climate record, with emphasis on optimizing the reconstruction to provide the highest possible spatial resolution at the lowest noise level. SMAP radiometer reconstruction on SMAP-standard grids is also considered. Simulation is used to optimize the reconstruction, and the results confirmed using actual data. A comparison of the performance of the Backus–Gilbert approach and the radiometer form of the Scatterometer Image Reconstruction algorithm is provided. These are compared to the conventional drop-in-the-bucket gridded imaging.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes From
           Heterogeneous Sea Ice Surfaces
    • Authors: Jack C. Landy;Michel Tsamados;Randall K. Scharien;
      Pages: 4164 - 4180
      Abstract: Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multiscale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler synthetic aperture radar (SAR) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facet-based models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, and roll) and sea ice properties (physical properties, roughness, and presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm—potentially underestimating sea ice thickness by around 50 cm. We present a set of “ideal” waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness, and snow depth, from SAR altimeter observations.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Through-the-Multilayered Wall Imaging Using Passive Synthetic Aperture
           Radar
    • Authors: Hajar Abedi;Bijan Zakeri;
      Pages: 4181 - 4191
      Abstract: Most of the existing through-the-wall imaging (TWI) methods using synthetic aperture radar (SAR) tend to apply an active system. In this paper, a novel, passive SAR (PSAR), termed TWI-PSAR, is proposed, to focus the image of multi targets behind a single-/multilayered wall. TWI-PSAR would work in a bistatic configuration using wideband sources of opportunity and a single moving platform or a stationary linear array receiver. Incident angle and frequency are considered the parameters that influence TWI image directly. A stepped frequency transmitter with single incident angle is applied to investigate the incident angle effect. It could show the capability of small angle to suppress wall effects. Zero incident angle PSAR (Z-PSAR) is exploited in TWI for enhanced target identification and feature extraction as well as wall effect mitigation. In scenarios where background measurement might not be available or wall parameters are unknown for compensation, Z-PSAR could be adopted. Compared to other conventional imaging methods such as SAR and time reversal, numerical results show the superiority of the proposed TWI system in urgent situations with unknown wall parameters, employing free-space Green’s function. Moreover, to demonstrate the effectiveness of the proposed PSAR method in a real situation, sources of opportunity that are relatively wideband and aligned in several directions, such as analog TV, Digital Video Broadcasting—Terrestrial, GSM, and WiMAX, are used to image targets behind the wall. Also, Monte Carlo method is used to show the effectiveness of TWI-PSAR in different frequency and incident angle scenarios.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Coal Quality Exploration Technology Based on an Incremental Multilayer
           Extreme Learning Machine and Remote Sensing Images
    • Authors: Ba Tuan Le;Dong Xiao;Yachun Mao;Dakuo He;Jialiu Xu;Liang Song;
      Pages: 4192 - 4201
      Abstract: This paper proposes a new coal quality exploration method that detects coal quality in coal mining areas and explores and monitors the distribution and change of coal through remote sensing images. First, we collected a large number of coal and noncoal samples such as sandstones, shales, and coal gangues. Second, we measured the actual spectral data of these samples using a spectrometer. For coal mines, we used the chemical analysis method to quantify coal’s fixed carbon and categorize the coal mines into three types based on the fixed carbon content present in coal. Third, we collected satellite remote sensing images of coal mining areas and established spectral data relations between the measured spectral data of the samples and the remote sensing images. Fourth, we proposed an incremental multilayer learning machine algorithm and used the algorithm combined with spectral data to build a coal quality classification model to identify coal quality in remote sensing images. Finally, the model accurately described the distribution map of coal quality. Compared with traditional coal exploration methods, this method has the advantages of high speed, high accuracy, and low price.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Noise-Robust Motion Compensation for Aerial Maneuvering Target ISAR
           Imaging by Parametric Minimum Entropy Optimization
    • Authors: Jiadong Wang;Lei Zhang;Lan Du;Dongwen Yang;Bo Chen;
      Pages: 4202 - 4217
      Abstract: When a target is involved in maneuvering motion, the nonuniform 3-D rotation motion will cause a continuous change of image projection plane (IPP), which would induce 2-D spatial-variant phase errors. In this case, the inverse synthetic aperture (ISAR) image would be seriously blurred when using the traditional compensation methods. On the other hand, strong noise has been always challenging the conventional methods in motion parameters estimation and phase error compensation. In this paper, we propose a noise-robust compensation method to compensate the 2-D spatial-variant phase errors of the maneuvering target via using tracking information and parametric minimum entropy optimization. First, the maneuvering signal model is developed based on a 2-D spatial-variant model and a 3-D rotation motion model. Based on the developed signal model, a parametric entropy minimum optimization is established to estimate the rotation motion parameters. A gradient-based solver of this optimization is then adopted to iteratively find the global optimum. Meanwhile, in order to increase the robustness of this optimization under low SNR, an extended Kalman filter is adopted here for coarse motion estimation via using tracking information. By treating these estimated motion parameters as initial values, we can effectively prevent this optimization from trapping into a local optimum. Finally, the 2-D spatial-variant phase error can be iteratively compensated, and a well-focused ISAR image can be obtained. The proposed method has three main contributions: 1) it is applicable in the case of changing IPP; 2) it gives the exact expression of chip parameters; and 3) it can efficiently compensate the 2-D spatial-variant phase errors under low SNR. Experiments based on the simulated data and the real measured data prove the effectiveness and robustness of the proposed method.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral
           Anomaly Detection
    • Authors: Weiying Xie;Tao Jiang;Yunsong Li;Xiuping Jia;Jie Lei;
      Pages: 4218 - 4230
      Abstract: Anomaly detection is one of the most important applications of hyperspectral imaging technology. It is a challenging task due to the high dimensionality of hyperspectral images (HSIs), redundant information, noisy bands, and the limited capability of utilizing spatial information. In this paper, we address these problems and propose a novel anomaly detection method in HSIs. Our approach, called structure tensor and guided filter (STGF)-based strategy for anomaly detection, is based on the characteristics of HSIs. First, a novel band selection algorithm is proposed to reduce dimension, remove noisy bands, and select bands with effective information. Second, the selected bands are decomposed into two parts according to the characteristics of anomalies that are usually in a small area. Followed by this step, the backgrounds are removed through a simple differential operation for each selected band. Considering that not all of the bands provide the same contributions to anomaly detection, we then fuse the differential maps by a novel adaptive weighting method to obtain an initial detection map. Finally, GF is conducted to rectify the previous map under the condition that the neighboring pixels usually have quite strong correlations with each other. Experiments have been conducted on real-scene remote sensing HSI. Comparative analyses validate that the proposed STGF method presents superior performance in terms of detection accuracy and computational time.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Fast Cross-Range Scaling Algorithm for ISAR Images Based on the 2-D
           Discrete Wavelet Transform and Pseudopolar Fourier Transform
    • Authors: Dong Li;Chengxiang Zhang;Hongqing Liu;Jia Su;Xiaoheng Tan;Qinghua Liu;Guisheng Liao;
      Pages: 4231 - 4245
      Abstract: To better interpret the inverse synthetic aperture radar (ISAR) imaging results, it is highly desirable to present them in the homogeneous range-cross-range domain, rather than the conventional range-Doppler (RD) domain. This process is referred to as cross-range scaling and the rotating angle velocity (RAV) of the moving target must be estimated first to achieve that goal. In this paper, an efficient cross-range scaling approach based on 2-D discrete wavelet transform (2D-DWT) and pseudopolar fast Fourier transform (PPFFT) is developed. To be exact, first, 2D-DWT is applied to two sequential ISAR images to obtain the dominant feature points based on the fact that the ISAR images are usually redundant for estimating RAV. By doing so, the data dimensional reduction and noise suppression are also realized. After that, second, via the efficient PPFFT, two sequential RD ISAR images are mapped into the pseudopolar coordinate to convert the rotational motion into the translational motion along the pseudo angle direction. Finally, to estimate the RAV, a new normalized correlation cost function is constructed and the Golden section algorithm is employed to efficiently find the optimal RAV. Compared with the conventional methods, the advantages of the proposed method are threefold: 1) the rotation center of a target is no longer required prior; 2) without the interpolation operation and the utilization of data dimensional reduction via 2D-DWT, the computational complexity of the proposed method is significantly reduced; and 3) the accurate RAV estimation is achieved in the case of low signal-to-noise ratio condition. The results from both the simulated and the measured data demonstrate that the proposed approach outperforms the state-of-the-art algorithms in terms of the estimation accuracy and computational complexity.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Focusing Improvement of Curved Trajectory Spaceborne SAR Based on Optimal
           LRWC Preprocessing and 2-D Singular Value Decomposition
    • Authors: Jianlai Chen;Guang-Cai Sun;Mengdao Xing;Buge Liang;Yuexin Gao;
      Pages: 4246 - 4258
      Abstract: The curved trajectory can lead to severely 2-D spatial-variance in spaceborne synthetic aperture radar (SAR). The azimuth-variance makes the traditional frequency domain imaging algorithms for the straight trajectory based on the assumption of azimuth translational invariance invalid. To correct the severely 2-D spatial-variance in curved trajectory spaceborne SAR, this paper studies a frequency imaging algorithm based on an optimal linear range walk correction (LRWC) preprocessing and 2-D singular value decomposition (SVD). Before the correction of the 2-D spatial-variance, an optimal LRWC preprocessing is introduced to minimize the azimuth-variance. Subsequently, a range block-SVD is proposed to correct the range-variance and, thus, achieves the accurate range cell migration correction. Finally, the azimuth tandem-SVD method is used to correct the azimuth-variance and, thus, accomplishes the azimuth compression for the whole azimuth scene. Processing of the simulated data validates the effectiveness of the proposed algorithm.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Novel Approach to the Unsupervised Update of Land-Cover Maps by
           Classification of Time Series of Multispectral Images
    • Authors: Claudia Paris;Lorenzo Bruzzone;Diego Fernández-Prieto;
      Pages: 4259 - 4277
      Abstract: This paper presents an unsupervised approach that extracts reliable labeled units from outdated maps to update them using time series (TS) of recent multispectral (MS) images. The method assumes that: 1) the source of the map is unknown and may be different from remote sensing data; 2) no ground truth is available; 3) the map is provided at polygon level, where the polygon label represents the dominant class; and 4) the map legend can be converted into a set of classes discriminable with the TS of images (i.e., no land-use classes that require manual analysis are considered). First, the outdated map is adapted to the spatial and spectral properties of the MS images. Then, the method identifies reliable labeled units in an unsupervised way by a two-step procedure: 1) a clustering analysis performed at polygon level to detect samples correctly associated to their labels and 2) a consistency analysis to discard polygons far from the distribution of the related land-cover class (i.e., having high probability of being mislabeled). Finally, the map is updated by classifying the recent TS of MS image with an ensemble of classifiers trained using only the reference data derived from the map. The experimental results obtained updating the 2012 Corine Land Cover (CLC) and the GlobLand30 in Trentino Alto Adige (Italy) achieved 93.2% and 93.3% overall accuracy (OA) on the validation data set. The method increased the OA up to 18% and 11.5% with respect to the reference methods on the 2012 CLC and the GlobLand30, respectively.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • PSASL: Pixel-Level and Superpixel-Level Aware Subspace Learning for
           Hyperspectral Image Classification
    • Authors: Jie Mei;Yuebin Wang;Liqiang Zhang;Bing Zhang;Suhong Liu;Panpan Zhu;Yingchao Ren;
      Pages: 4278 - 4293
      Abstract: The performance of hyperspectral image (HSI) classification relies on the pixel information obtained from hundreds of contiguous and narrow spectral bands. Existing approaches, however, are limited to exploit an appropriate latent subspace for data representation within the pixel-level or superpixel-level. To utilize spectral information and spatial correlation among pixels in HSI and avoid the “salt-and-pepper” problem generated in the pixel-based HSI classification, a novel pixel-level and superpixel-level aware subspace learning method called PSASL is developed. The PSASL constructs the subspace learning framework based on the reconstruction independent component analysis algorithm. The spectral–spatial graph regularization and label space regularization are developed as the pixel-level constraints. To avoid the “salt-and-pepper” problem generated in the pixel-based classification methods, superpixel-level constraints are introduced for integrating the data representations defined in the subspace and class probabilities of the pixels in the same superpixel. The subspace learning and the pixel-level regularization are combined with the superpixel-level regularization to form a unified objective function. The solution to the objective function is efficiently achieved by employing a customized iterative algorithm, and it converges very fast. A discriminative data representation and a universal multiclass classifier are learned simultaneously. We test the PSASL on three widely used HSI data sets. Experimental results demonstrate the superior performance of our method over many recently proposed methods in HSI classification.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • An Efficient and Scalable Framework for Processing Remotely Sensed Big
           Data in Cloud Computing Environments
    • Authors: Jin Sun;Yi Zhang;Zebin Wu;Yaoqin Zhu;Xianliang Yin;Zhongzheng Ding;Zhihui Wei;Javier Plaza;Antonio Plaza;
      Pages: 4294 - 4308
      Abstract: The large amount of data produced by satellites and airborne remote sensing instruments has posed important challenges to efficient and scalable processing of remotely sensed data in the context of various applications. In this paper, we propose a new big data framework for processing massive amounts of remote sensing images on cloud computing platforms. In addition to taking advantage of the parallel processing abilities of cloud computing to cope with large-scale remote sensing data, this framework incorporates task scheduling strategy to further exploit the parallelism during the distributed processing stage. Using a computation- and data-intensive pan-sharpening method as a study case, the proposed approach starts by profiling a remote sensing application and characterizing it into a directed acyclic graph (DAG). With the obtained DAG representing the application, we further develop an optimization framework that incorporates the distributed computing mechanism and task scheduling strategy to minimize the total execution time. By determining an optimized solution of task partitioning and task assignments, high utilization of cloud computing resources and accordingly a significant speedup can be achieved for remote sensing data processing. Experimental results demonstrate that the proposed framework achieves promising results in terms of execution time as compared with the traditional (serial) processing approach. Our results also show that the proposed approach is scalable with regard to the increasing scale of remote sensing data.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing
    • Authors: Yuanchao Su;Jun Li;Antonio Plaza;Andrea Marinoni;Paolo Gamba;Somdatta Chakravortty;
      Pages: 4309 - 4321
      Abstract: Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Time-Series Retrieval of Soil Moisture Using CYGNSS
    • Authors: Mohammad M. Al-Khaldi;Joel T. Johnson;Andrew J. O’Brien;Anna Balenzano;Francesco Mattia;
      Pages: 4322 - 4331
      Abstract: Time-series retrievals of soil moisture obtained from the Cyclone Global Navigation Satellite System (CYGNSS) constellation are presented. The retrieval approach assumes that vegetation and roughness changes occur on timescales longer than those associated with soil moisture changes to allow soil moisture sensing in the presence of vegetation and surface roughness contributions as well as the varying incidence angles associated with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) systems. The approach is focused on incoherent scattering from land surfaces due to the expectation that coherent land surface returns arise primarily from inland water body contributions that are not directly representative of soil moisture. An approach for discarding coherent CYGNSS measurements is therefore developed and described. Because the approach requires the retrieval of $N$ temporal soil moisture samples at a given location but uses only $N-1$ ratios of CYGNSS measured quantities, ancillary information is incorporated in the retrieval through the use of maximum and minimum monthly soil moisture maps obtained from the Soil Moisture Active Passive (SMAP) mission. Retrieved soil moistures are presented for the 6-month period December 2017–May 2018 and are compared against values reported by the SMAP mission. The comparisons suggest that there exists the potential for using spaceborne GNSS-R systems for global soil moisture retrievals with an rms error on the order of 0.04 cm3/cm3 over varied terrain.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Planetary Boundary Layer Height Detection Using Mountaintop-Based GNSS
           Radio Occultation Signal Amplitude
    • Authors: Bo Han;Yu Morton;Erry Gunawan;Dongyang Xu;
      Pages: 4332 - 4348
      Abstract: Global Navigation Satellite System (GNSS) Radio Occultation (RO) is an atmospheric remote sensing technique that improves global weather forecasting, climate monitoring, and ionospheric studies. Planetary boundary layer height (PBLH) is a crucial parameter in modeling the troposphere. Space-based GNSS RO has been used in detecting the PBLH with receivers onboard low earth orbit satellites. This paper presents a method of PBLH detection using GNSS signal amplitude measured by a mountaintop-based RO (MRO) system on the summit of Haleakala, Hawaii. The estimated PBLHs are comparable with those derived from space-based RO measurements, space-borne lidar, and local radiosonde profiles. With advantages such as having dense temporal and spatial coverage, low-cost, and an easy-to-implement algorithm, the MRO-based signal amplitude method can be a useful addition to existing methods and could contribute to regional weather study.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • CoSpace: Common Subspace Learning From Hyperspectral-Multispectral
           Correspondences
    • Authors: Danfeng Hong;Naoto Yokoya;Jocelyn Chanussot;Xiao Xiang Zhu;
      Pages: 4349 - 4359
      Abstract: With a large amount of open satellite multispectral (MS) imagery (e.g., Sentinel-2 and Landsat-8), considerable attention has been paid to global MS land cover classification. However, its limited spectral information hinders further improving the classification performance. Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to MS ones. To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification. By locally aligning the manifold structure of the two modalities, CoSpace linearly learns a shared latent subspace from hyperspectral-MS (HS-MS) correspondences. The MS out-of-samples can be then projected into the subspace, which are expected to take advantages of rich spectral information of the corresponding hyperspectral data used for learning, and thus leads to a better classification. Extensive experiments on two simulated HS-MS data sets (University of Houston and Chikusei), where HS-MS data sets have tradeoffs between coverage and spectral resolution, are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Scalable One-Pass Self-Representation Learning for Hyperspectral Band
           Selection
    • Authors: Xiaohui Wei;Wen Zhu;Bo Liao;Lijun Cai;
      Pages: 4360 - 4374
      Abstract: For applications based on hyperspectral imagery (HSI), selecting informative and representative bands without the degradation of performance is a challenging task in the context of big data. In this paper, an unsupervised band selection method, scalable one-pass self-representation learning (SOP-SRL), is proposed to address this problem by processing data in a streaming fashion without storing the entire data. SOP-SRL embeds band selection into a scalable self-representation learning, which is formulated as an adaptive linear combination of regression-based loss functions, with the row-sparsity constraint. To further enhance the representativeness of bands, the local similarity between samples constructed by the selected bands is dynamically measured by means of graph-based regularization term in the embedded space. Moreover, a cache with memory function that reflects the quality of bands in the historical data is designed to keep the consistency between data coming at different times and guide subsequent band selection. An efficient algorithm is developed to optimize the SOP-SRL model. The HSI classification is conducted on three public data sets, and the experimental results validate the superiority of SOP-SRL in terms of performance and time when compared with other state-of-the-art band selection methods.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Residual RCM Correction for LFM-CW Mini-SAR System Based on Fast-Time
           Split-Band Signal Interferometry
    • Authors: Xikai Fu;Bingnan Wang;Maosheng Xiang;Shuai Jiang;Xiaofan Sun;
      Pages: 4375 - 4387
      Abstract: A linear frequency modulation continuous-wave mini-synthetic aperture radar (SAR) system mounted on small aircrafts promises a high-flexibility and cost-effective microwave remote sensing technology. However, the mini-SAR system suffers from considerable trajectory deviations due to aircraft’s lightweight, low flight height, and limited capacity for a high-accuracy inertial measurement unit (IMU). With the rapid increasing requirements for resolution, the residual range cell migration (RCM) exceeds a single range cell. Under such circumstances, traditional autofocus algorithms fail to guarantee well-focused mini-SAR images and further accurate interferometric SAR (InSAR) applications. To solve these problems, this paper proposed a novel residual RCM correction scheme for a mini-SAR system mounted on small aircrafts without high-accuracy IMU. The core idea is to estimate the misalignments of adjacent range profiles based on fast-time split-band signal interferometry at each azimuth time and integrate them with time to obtain an estimation of residual RCM. The proposed method promises a high-accuracy misalignment estimation result without resampling operation in the traditional cross-correlation methods. Simulation and experimental results show the improvement of mini-SAR image focusing quality and refinement of coherence map between the master and slave images for InSAR applications, which demonstrated the effectiveness and reliability of our proposed residual RCM correction scheme for the mini-SAR system.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Optical Properties of Reflected Light From Leaves: A Case Study From One
           Species
    • Authors: Zhongqiu Sun;Di Wu;Yunfeng Lv;Shan Lu;
      Pages: 4388 - 4406
      Abstract: The reflection property of targets is the fundamental signal for applications of optical remote sensing of the earth’s surface. In this paper, we measured the photometric and polarimetric characteristics of 15 leaves with different properties from one plant species (i.e., Pachira aquatica) using a laboratory goniospectrometer system and used the bidirectional reflectance factor (BRF) and the bidirectional polarized reflectance factor (BPRF) to describe the reflection of these leaf samples. The results illustrated that the BRF can be replaced by the $I$ parameter reflectance factor ( $I$ pRF) when the extinction of the polarizer is considered. Subsequently, the BRF model was fit to the $I$ pRF measurements at selected wavelengths, and the inverted refractive index was used in a BPRF model, which has been proposed to simulate the polarization of surfaces. We found that the modeled photometric results of all the leaves matched well with our measurement results over all the measurement directions, while the modeled polarimetric results of the leaves gave a good agreement with the measurements at the forward scattering directions. Moreover, the degree of linear polarization (Dolp) of the leaf, which is derived from the ratio between BPRF and $I$ pRF, can also be effectively computed by the combination of BPRF and BRF models in the forward scattering directions. These findings suggested that more attention should be dedicated to the combination of BRF and BPRF of leaves in the future because we can completely describe the essential optical properties of the light reflected by leaves via the polarimetric measurement. This paper indicates that the polarimetric measurement-is a beneficial method for optical remote sensing applications and helps us deepen the understanding of the optical properties of leaves.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Brightness Temperatures From Very Lossy Medium With Near-Field Bistatic
           Transmission Coefficients
    • Authors: Zhi-Hong Lai;Jean-Fu Kiang;
      Pages: 4407 - 4416
      Abstract: The scattering fields from a very lossy medium with a flat interface are computed by using a finite-difference time-domain method. Near-field bistatic transmission coefficients (BTCs) are proposed, and the Planck’s law is extended to nondispersive lossy medium to compute the brightness temperatures from the lossy medium. The reciprocity relation on BTCs is utilized to reduce the computational load. The efficacy of the proposed method is verified by comparing the simulation results with the literature.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar
           With Application to Full-Waveform Inversion
    • Authors: Iraklis Giannakis;Antonios Giannopoulos;Craig Warren;
      Pages: 4417 - 4426
      Abstract: The simulation, or forward modeling, of ground penetrating radar (GPR) is becoming a more frequently used approach to facilitate the interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3-D electromagnetic (EM) solvers, such as the ones based on the finite-difference time-domain (FDTD) method, are still computationally demanding for simulating realistic GPR problems. We have developed a novel near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. The ML framework uses an innovative training method that combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large data set of modeled GPR responses from our FDTD simulation software. The ML-based forward solver is parameterized for a specific GPR application, but the framework can be applied to many different classes of GPR problems. To demonstrate the novelty and computational efficiency of our ML-based GPR forward solver, we used it to carry out FWI for a common infrastructure assessment application—determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data and found a good level of accuracy in determining the rebar location, size, and surrounding material properties from both data sets. The combination of the near-real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3-D EM solvers, and the accuracy of our ML-based forward model is a significant step toward commercially viable applications of FWI of GPR.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Top Cloud Motion Field of Typhoon Megi-2016 Revealed by GF-4 Images
    • Authors: Jianguo Liu;Gang Zheng;Jingsong Yang;Juan Wang;
      Pages: 4427 - 4444
      Abstract: Gaofen-4 (GF-4) is the first high-resolution geostationary satellite of China, launched on December 29, 2015. Its visible-near infrared optical sensor is capable of imaging the earth at 50 m resolution, covering a 512 km $times512$ km area with a minimum imaging interval of 20 s. More than 300 GF-4 images, taken in four sequences with imaging rates of 36 and 69 s per scene, captured the development of Typhoon Megi-2016—from its peak in the afternoon of September 26, 2016 to its dispersion two days later on September 28. These consecutive images recorded the motion field of the typhoon’s top clouds nearly continuously. By using advanced image matching technology, the motion has been estimated for every pixel, at subpixel accuracy from image pairs with 179 and 206 s intervals. The process has generated time series of atmospheric motion vector (AMV) fields at 50 m spatial resolution for the whole imaged area. It is the first time, to our knowledge, that such high-resolution and nearly continuous AMV data of a typhoon system have been produced. The data provide accurate measurements of the typhoon top cloud motion speed and direction, and reveal quantitative details of the motion field spatiotemporal evolution at high altitude. The finding that the high altitude top cloud motion speed is significantly lower than that recorded at low altitude below the planetary boundary layer is of scientific value and further exploitation of the motion data can lead to a better understanding of typhoon dynamics and thus improve cyclone modeling.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Open Set Incremental Learning for Automatic Target Recognition
    • Authors: Sihang Dang;Zongjie Cao;Zongyong Cui;Yiming Pi;Nengyuan Liu;
      Pages: 4445 - 4456
      Abstract: Incremental learning methods update the existing model with new knowledge when the target data increase continuously. Open set recognition (OSR) algorithms provide classifiers with a rejection option so that the new untrained target type is identified. In this paper, an open set incremental learning method is introduced for automatic target recognition, which is able to recognize and learn the new unknown classes continually. The proposed method, open set model with incremental learning (OSmIL), is an ensemble classifier so it is able to be updated only by the new data. For saving the computational time and storage source, a new exemplar selection method is introduced for model simplifying. Edge samples are selected to cover training classes; as a result, the model size is deduced and controlled. Moreover, because extreme value theory (EVT) is suitable to fit a classification model that includes open space risk, the decision function based on EVT makes an open set classifier for identifying the new classes. Experimental results demonstrate that the proposed OSmIL outperforms the other state of the arts on the accuracy of multiclass OSR. And OSmIL can maintain good accuracy and efficiency in the incremental learning experiment set.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Novel Rank Approximation Method for Mixture Noise Removal of
           Hyperspectral Images
    • Authors: Hailiang Ye;Hong Li;Bing Yang;Feilong Cao;Yuanyan Tang;
      Pages: 4457 - 4469
      Abstract: Mixture noise removal is a fundamental problem in hyperspectral images’ (HSIs) processing that holds significant practical importance for subsequent applications. This problem can be recast as an approximation issue of a low-rank matrix. In this paper, a novel smooth rank approximation (SRA) model is proposed to cope with these mixture noises for HSIs. The crux idea is to devise a general smooth function under some assumptions to directly approximate the rank function, which attempts to explore a closer approximation than conventional methods. This new optimization model can be easily solved by the convex analysis tool and can remove the mixture noises of HSIs quickly and effectively. Subsequently, we give a feasible iterative algorithm, and the corresponding convergence analysis is discussed mathematically. Experimental results from the simulated data set as well as real data sets illustrate that the proposed SRA method significantly outperforms the state-of-the-art methods on HSI denoising.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Back-Projection Tomographic Framework for VHR SAR Image Change Detection
    • Authors: Elías Méndez Domínguez;Christophe Magnard;Erich Meier;David Small;Michael E. Schaepman;Daniel Henke;
      Pages: 4470 - 4484
      Abstract: Information on 3-D structure expands the scope of change detection applications, for example, in urban studies, human activity, and forest monitoring. Current change detection methods do not fully consider the specifics of SAR data or the properties of the corresponding image focusing techniques. We propose a three-stage method complementing the properties of 2-D and 3-D very high-resolution (VHR) synthetic aperture radar imagery to improve the performance of 2-D only approaches. The method takes advantage of back-projection tomography to ease translation of the 2-D location of the targets into their corresponding 3-D location and vice versa. Detection of changes caused by objects with a small vertical extent is based on the corresponding backscatter difference, while changes caused by objects with a large vertical extent are detected with both backscatter and height difference information combined in a conditional random field. Using multitemporal images, the kappa coefficient improved by a factor of two in comparison with traditional schemes.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Recent Regime Shifts in Mineral Dust Trends Over South Asia From Long-Term
           CALIPSO Observations
    • Authors: N. B. Lakshmi;S. Suresh Babu;Vijayakumar S. Nair;
      Pages: 4485 - 4489
      Abstract: Mineral dust aerosols have significant implications on the regional-hydroclimate, especially over the regions located downwind of dust sources. During the premonsoon season, much of South Asia is characterized by enhanced aerosol loading favored by the transport of mineral dust from the desert regions of West Asia/Northwest India. Vertically resolved backscatter measurements at dual polarization from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to estimate the dust optical depth over the Indian region. Using a decade long data set of CALIOP observations, a shift in long-term trend of dust over the Northwest India, Indo-Gangetic Plain, and West Asia is demonstrated. The decreasing trend in dust loading over the Indian region reversed to increasing after 2013. The interannual variability in premonsoon dust optical depth over Northwest India is found to be associated with winter time rainfall. The interannual variation of tropospheric temperature anomalies over Northwest India did not show a direct correlation with mineral dust loading.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Multiobjective Sparse Subpixel Mapping for Remote Sensing Imagery
    • Authors: Mi Song;Yanfei Zhong;Ailong Ma;Ruyi Feng;
      Pages: 4490 - 4508
      Abstract: Subpixel mapping (SPM) of remote sensing imagery is aimed at generating a classification map with a finer spatial resolution based on the abundance maps. The sparse subpixel mapping (SSM) method reformulates the SPM problem into a spatial pattern linear regression problem based on the preconstructed subpixel patch dictionary. However, in the SSM model, the optimization of the ${L}0$ -norm is a nonconvex NP-hard problem, so the ${L}1$ -norm is used to replace the ${L}0$ -norm to obtain an approximate solution, and the selection of the optimal weight parameter between multiple terms is difficult. Thus, in this paper, a novel multiobjective SSM (MOSSM) framework for remote sensing imagery is proposed, which transforms the SSM problem into a multiobjective optimization problem. In MOSSM, first, the sparsity term is accurately modeled using the ${L}0$ -norm instead of the ${L}1$ -norm to avoid the potential errors caused by the ${L}1$ -norm, and an evolutionary algorithm is used to directly optimize the ${L}0$ -norm. Second, a subfitness-based multiobjective evolutionary algorithm is employed to simultaneously optimize the fidelity term, the sparsity term, and the spatial prior term, and to generate a set of optimal sparse coefficients to balance these three terms. Thus, there is no need to determine sensitive weight parameters. Finally, two spatial prior terms, which can be applied to the overcomplete dictionary, are presented in the proposed MOSSM-TV and MOSSM-L algorithms -o incorporate the spatial correlation of subpixels. Experiments were conducted with two synthetic images and two real data sets, and the results were compared with those of ten other SPM algorithms to demonstrate the effectiveness of the proposed method.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Unsupervised Kernelized Correlation-Based Hyperspectral Unmixing With
           Missing Pixels
    • Authors: Kazi Tanzeem Shahid;Ioannis D. Schizas;
      Pages: 4509 - 4520
      Abstract: In this paper, a novel framework is developed that performs unmixing in a set of hyperspectral pixels which contain mixtures of pure materials. This novel algorithm utilizes statistical correlation present among the mixed data to evaluate the contribution levels (abundances) of each pure material, along with their spectral responses (endmembers). Norm-one regularization along with kernel transformations is employed to construct a constrained regularized and kernelized correlation framework that can estimate the contribution of the pure materials in the pixels. A novel combination of coordinate and gradient descent along with the Lagrange multipliers method enables the recursive and efficient estimation of abundances facilitating a least-squares estimation of the pure materials’ spectral responses. Novel unsupervised kernel selection is performed by exploiting eigenvalue decomposition, while irrelevant spectral bands are clipped utilizing variance measures. Extensive numerical tests using both real hyperspectral as well as synthetic data generated using real data sets show that not only does our novel unsupervised method outperform existing supervised and unsupervised techniques, but it is also highly robust even in the presence of data corruption originating from dead (missing) pixels as well as in the presence of lower degree of purities.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Solar Influence on Fire Radiative Power Retrieved With the Bispectral
           Method
    • Authors: Doris Klein;Rudolf Richter;Christian Strobl;Daniel Schläpfer;
      Pages: 4521 - 4528
      Abstract: Fire radiative power (FRP) is a key product to quantify active fires, which indicates fuel consumption and fire emissions. In the case of the bispectral method, it can be calculated from remote sensing data if a midinfrared ( $3.8~mu text{m}$ ) and thermal infrared channel ( $sim 10~mu text{m}$ ) are available. While different uncertainty sources have been investigated, the quantitative evaluation of the FRP error as a function of reflected solar radiation is still missing. The ground-reflected solar radiance adds an unknown signal component to the at-sensor radiance during the daytime, which influences the fire detection algorithm as well as the FRP product. FRP errors can reach up to 5%–15% for smoldering fire temperatures of 400–500 K, which is a systematic bias. Errors decrease with increasing temperature and for temperatures higher than 700 K, i.e., flaming fires, the FRP bias is less than 2%. The evaluation is performed for the TET-1 instrument of DLR’s FireBIRD mission using the bispectral method.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • On the Effect of Polarization and Incidence Angle on the Estimation of
           Significant Wave Height From SAR Data
    • Authors: Michael J. Collins;Meng Ma;Mohammed Dabboor;
      Pages: 4529 - 4543
      Abstract: Significant wave height is an extremely important descriptor of the ocean wave field. We have implemented the CWAVE algorithm using linear regression, with elastic net term selection, and single-layer feed-forward neural network using buoy observations and RADARSAT-2 Fine Quad image data as model inputs. We used a number of standard performance metrics and found that the neural network models comprehensively outperformed the regression models. We explored the effect of incidence angle and polarization on model performance and found that the most accurate models were implemented within incidence angle bins between 1° and 2°, rather than including incidence angle as an independent variable. We found that the performance of copol (horizontal–horizontal, vertical–vertical, and RL) and hybrid-pol (right-circular-horizontal and right-circular-vertical) channels was comparable, and that these channels outperformed cross-pol channels (horizontal–vertical and right-circular–right-circular). The accuracy of our $H_{s}$ estimates was significantly higher than other published linear regression and neural network results. We demonstrate that a major factor in improving the accuracy of $H_{s}$ estimation is to use buoy observations rather that operation wave model hindcasts as training data. We demonstrate an application of our model by creating two high-resolution $H_{s}$ maps.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Magnetic Resonance Tomography for 3-D Water-Bearing Structures Using a
           Loop Array Layout
    • Authors: Chuandong Jiang;Guanfeng Du;Tingting Lin;
      Pages: 4544 - 4557
      Abstract: Magnetic resonance tomography (MRT) is a technique that is used in the 2-D or 3-D detection and imaging of subsurface water-bearing structures based on the principle of surface nuclear magnetic resonance. Currently, the research and application of 3-D MRT is still limited by low measurement efficiency and image resolution. In this paper, a new loop array layout that consists of a coincident transmitting (Tx) and receiving (Rx) loop and an array of Rx loops is proposed to achieve high-efficiency MRT data acquisition and 3-D imaging. A number of water-bearing structures with various shapes (X, L, + and S models) are simulated based on the forward modeling of separated Tx and Rx loops with arbitrary geometries and topographies. Using the complex QT inversion scheme, images of these structures produced by 3-D MRT with the loop array layout are examined. The numerical simulation experiment shows that in low noise conditions, the water content distribution pattern obtained by inversion can reflect the fine details of the water-bearing structure, and an accurate relaxation time ( $T_{2}^{*}$ ) is provided. As the noise level increases, 3-D MRT images gradually become blurry. Nevertheless, increasing the number of Rx loop arrays can significantly improve the image resolution. Finally, the feasibility of practical applications of 3-D MRT with the loop array layout and feasible methods of improving measurement are discussed.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Exploiting Adiabatic Pulses With Prepolarization in Detection of
           Underground Nuclear Magnetic Resonant Signals
    • Authors: Tingting Lin;Yujing Yang;Ying Yang;Ling Wan;Fei Teng;
      Pages: 4558 - 4567
      Abstract: During the excavation of underground tunnels and in ore mining, accidents related to water bursts occasionally occur. As the only technique used for the direct detection of groundwater, the nuclear magnetic resonant (NMR) method has advantages for the detection of disaster-inducing water flows. Unfortunately, the amplitudes of underground NMR (UNMR) signals are in the range of some tens of nanovolts ( $10^{-9}$ V) or even picovolts ( $10^{-12}$ V), and thus extremely susceptible to environmental noise. By increasing the macromagnetic moment of groundwater, both adiabatic pulses and prepolarization (PP) methods have been employed in surface NMR. However, when using either method, it is difficult to achieve substantial signal enhancements over large volumes. For maximum signal amplitudes, we integrated these two approaches and derived the forward formulas with adiabatic pulses under PP for UNMR. In comparison with existing methods, this new model can achieve high sensitivity and broad responses. (A 6-m antenna attains a $10^{-5}$ V signal level for a homogeneous subsurface with 0.2 $text{m}^{3}/text{m}^{3}$ water content.) Thus, better resolution could also be provided even in a high-noise place. Overall, the large NMR signals and high resolutions make the combination of adiabatic pulses with PP a valuable approach, which is expected to open up a new application for UNMR.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Hybrid 3-D Electromagnetic Method for Induction Detection of Hydraulic
           Fractures Through a Tilted Cased Borehole in Planar Stratified Media
    • Authors: Yuan Fang;Junwen Dai;Qiwei Zhan;Yunyun Hu;Mingwei Zhuang;Qing Huo Liu;
      Pages: 4568 - 4576
      Abstract: As one of the most important nondestructive characterization techniques, electromagnetic (EM) methods can be used in the subsurface fracture detection, especially for hydraulic fracture evaluation in unconventional petroleum exploration and development. The multiscale nature of long but extremely thin 3-D fractures is difficult for conventional EM modeling methods such as the finite element method (FEM) in numerical simulation. The problem becomes even more challenging when the effects of tilted borehole, casing, and planar stratified media need to be considered. So far, modeling a tilted borehole in layered media is still a major challenge for conventional methods. In this paper, we present the hybrid numerical mode-matching method with the stabilized biconjugate gradient fast Fourier transform method as the forward modeling algorithm that can efficiently model 3-D fractures in planar stratified media with a cased borehole environment. Numerical results validate the accuracy of the hybrid forward method and show orders of magnitude higher efficiency of this forward solver than the FEM.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Effects of Compression on Remote Sensing Image Classification Based on
           Fractal Analysis
    • Authors: Zhenzhong Chen;Ye Hu;Yingxue Zhang;
      Pages: 4577 - 4590
      Abstract: Image compression is essential for remote sensing due to the large volume of produced remote sensing imagery and system’s limited transmission or storage capacity. As one of the most important applications, classification might be affected due to the introduced distortion during compression. Hence, we perform a quantitative study on the effects of compression on remote sensing image classification and propose a method to estimate the remote sensing image classification accuracy based on fractal analysis. Multiscale feature extraction is performed and a multiple kernel learning approach is proposed accordingly. The experimental results on our established database indicate that the classification accuracy predicted by our method exhibits high consistency with the ground truth and our method shows its superiority when compared with other classical reference algorithms.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Complete-Q Model for Poro-Viscoelastic Media in Subsurface Sensing:
           Large-Scale Simulation With an Adaptive DG Algorithm
    • Authors: Qiwei Zhan;Mingwei Zhuang;Zhennan Zhou;Jian-Guo Liu;Qing Huo Liu;
      Pages: 4591 - 4599
      Abstract: In this paper, full mechanisms of dissipation and dispersion in poro-viscoelastic media are accurately simulated in time domain. Specifically, four Q values are first proposed to depict a poro-viscoelastic medium: two for the attenuation of the bulk and shear moduli in the solid skeleton, one for the bulk modulus in the pore fluid, and the other one for the solid-fluid coupling. By introducing several sets of auxiliary ordinary differential equations, the Q factors are efficiently incorporated in a high-order discontinuous Galerkin algorithm. Consequently, in the mathematical sense, the Riemann problem is exactly solved, with the same form as the inviscid poroelastic material counterpart; in the practical sense, our algorithm requires nearly negligible extra time cost, while keeping the governing equations almost unchanged. Parenthetically, an arbitrarily nonconformal-mesh technique, in terms of both h- and p-adaptivity, is implemented to realize the domain decomposition for a flexible algorithm. Furthermore, our algorithm is verified with an analytical solution for the half-space modeling. A validation with an independent numerical solver, and an application to a large-scale realistic complex topography modeling demonstrate the accuracy, efficiency, flexibility, and capability in realistic subsurface sensing.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • New Temporal and Spectral Unfiltering Technique for ERBE/ERBS WFOV
           Nonscanner Instrument Observations
    • Authors: Alok K. Shrestha;Seiji Kato;Takmeng Wong;Paul Stackhouse;Robert P. Loughman;
      Pages: 4600 - 4611
      Abstract: Earth Radiation Budget Experiment (ERBE) Wide-Field-of-View (WFOV) nonscanner instrument onboard Earth Radiation Budget Satellite (ERBS) provided critical 15-year outgoing broadband irradiances at the top of atmosphere (TOA) from 1985 to 1999 for studying Earth’s climate. However, earlier studies show that the uncertainty in this radiation data set (Ed3) is significantly higher after the Mt. Pinatubo eruption in 1991 and satellite battery issue in 1993. Furthermore, Lee et al. showed that the transmission of ERBS WFOV shortwave dome degraded due to exposure to direct sunlight. To account for this degradation, a simple time-dependent but spectral-independent correction model was implemented in the past. This simple spectral-independent model did not completely remove the shortwave sensor artifact as seen in the temporal growth of the tropical mean day-minus-night longwave irradiance. A new temporal–spectral-dependent correction model of shortwave dome transmissivity loss similar to that used in the Clouds and the Earth’s Radiant Energy System (CERES) project is developed and applied to the 15-year ERBS WFOV data. This model is constrained by the solar transmission obtained from ERBS WFOV shortwave nonscanner instrument observations of the Sun during biweekly in-flight solar calibration events. This new model is able to reduce the reported tropical day-minus-night longwave irradiance trend by ≈34%. In addition, the slope of this new trend is observed to be consistent over different regions. The remaining trend is accounted using a postprocess Ed3Rev1 correction. Furthermore, the time series analysis of these data over the Libya-4 desert site showed that the shortwave data are stable to within 0.7%.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Automatic Source Localization and Attenuation of Seismic Interference
           Noise Using Density-Based Clustering Method
    • Authors: Pengcheng Xu;Wenkai Lu;Benfeng Wang;
      Pages: 4612 - 4623
      Abstract: Marine seismic data may be contaminated by external source interference noise (ESIN) in some cases. These ESINs can be suppressed automatically, provided we can localize these external sources correctly. In this paper, we propose an automatic method to localize the external sources using a density-based clustering method and then suppress the ESINs according to the sources. In a shot gather, the time delays between three randomly selected seismic traces are used to calculate the location of one potential external source directly. Since there are many seismic traces in one shot gather, we can get a lot of estimates of the external source locations. In general, some of these locations are falsely detected sources. Assuming that the location of the external source is fixed or slowly changes during one seismic shot acquisition, multiple true estimates of an external source location, which are obtained from different trace groups, should focus together. In contrast, the false locations are arbitrarily distributed. Therefore, a density-based clustering method is applied to obtain the final source location estimate. Since each potential source corresponds to one cluster, we find out the strongest ESIN corresponding to the cluster with maximum sample number to ensure the accuracy. After that, the detected ESIN is flattened and then extracted by singular value decomposition. This method is an iterative method, and in each iteration, one ESIN is suppressed. And the high-pass filter is optional to detect the weak ESINs and protect the valid signals. The synthetic data example and real field marine data example prove that the proposed method can localize the external sources accurately and suppress multiple ESINs effectively.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Proximal Radar Sensors for Precision Viticulture
    • Authors: Dominique Henry;Hervé Aubert;Thierry Véronèse;
      Pages: 4624 - 4635
      Abstract: In this paper, we report the accurate estimation of vine grape yield from a 3-D radar imagery technique. Three ground-based frequency-modulated continuous-wave radars operating, respectively, at 24, 77, and 122 GHz are used for the contact-less estimation of grape mass in vineyards. The 3-D radar images are built from the beam scanning of the vine plants and allow estimating the mass of grapes from the computation of appropriate statistical estimators. These estimators are derived from the measured polarization and magnitude of radar echoes. It is shown that the estimation of grape mass from the proposed ground-based radar imagery technique at millimeter-wave frequency range may be accurate within 1%.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • An Operational Approach for Generating the Global Land Surface Downward
           Shortwave Radiation Product From MODIS Data
    • Authors: Xiaotong Zhang;Dongdong Wang;Qiang Liu;Yunjun Yao;Kun Jia;Tao He;Bo Jiang;Yu Wei;Han Ma;Xiang Zhao;Wenhong Li;Shunlin Liang;
      Pages: 4636 - 4650
      Abstract: Surface shortwave net radiation (SSNR) and surface downward shortwave radiation (DSR) are the two surface shortwave radiation components in earth’s radiation budget and the fundamental quantities of energy available at the earth’s surface. Although several global radiation products from global circulation models, global reanalyses, and satellite observations have been released, their coarse spatial resolutions and low accuracies limit their application. In this paper, the Global LAnd Surface Satellite (GLASS) DSR product was generated from the Moderate Resolution Imaging Spectroradiometer top-of-atmosphere (TOA) spectral reflectance based on a direct-estimation method. First, the TOA reflectances were derived based on the atmospheric radiative transfer simulations under different solar/view geometries; second, a linear regression relationship between the TOA reflectance and SSNR was developed under various atmospheric conditions and surface properties for different solar/view geometries; third, the coefficients derived from the linear regression were used to compute the SSNR; and finally, the DSR was estimated using the SSNR estimates and broadband albedo at the surface. A 13-year (2003–2015) GLASS DSR product was generated at a 5-km spatial resolution and 1-day temporal resolution. Compared with the ground measurements collected from 525 stations from 2003 to 2005 around the world, the model-computed SSNR (DSR) had an overall bias of 8.82 (3.72) W/m2 and a root mean square error of 28.83 (32.84) W/m2 at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m2 with a standard deviation of 0.8 W/m2 over a 13-year (2003–2015) period.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Theory of SAR Signature Defocus Morphology for Arbitrary 3-D Target Motion
           Over Terrain Relief
    • Authors: David Alan Garren;
      Pages: 4651 - 4658
      Abstract: This paper generates analytic equations for predicting the 2-D defocus morphology of the signature smears within spotlight synthetic aperture radar imagery, which are induced by targets having arbitrary 3-D motion. One primary application of such 3-D target motion is to enable the inclusion of terrain relief in the signatures smears due to surface targets. These mathematical techniques can predict the detailed imagery shapes of such induced signature smears, including width and interference effects. These methods provide an accurate and effective tool in predicting the shape, extent, and location of the defocused smears due to surface targets that are subject to terrain relief.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Radar Image Series Denoising of Space Targets Based on Gaussian Process
           Regression
    • Authors: Xueru Bai;Xin Peng;
      Pages: 4659 - 4669
      Abstract: We address the problem of image series denoising for high-resolution radar in a nonparametric Bayesian framework. By exploiting the characteristics of amplitude variation at different pixels in the image series, we impose the Gaussian process (GP) model to the corresponding time series of each pixel and achieve effective image series denoising by GP regression. Particularly, the model parameters are solved conveniently by the maximum likelihood estimation. Compared with available denoising techniques in the data domain, spatial domain, and image frequency domain, the proposed method has exhibited more flexibility in data description and better performance in structure preserving and denoising, especially in low signal-to-noise ratio scenarios.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Method Based on Temporal Component Decomposition for Estimating 1-km
           All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared
           and Passive Microwave Observations
    • Authors: Xiaodong Zhang;Ji Zhou;Frank-Michael Göttsche;Wenfeng Zhan;Shaomin Liu;Ruyin Cao;
      Pages: 4670 - 4691
      Abstract: Land surface temperature (LST) is a key variable at the land–atmosphere boundary. For many research projects and applications an all-weather LST product at moderate spatial resolution (e.g., 1 km) would be highly useful, especially in frequently cloudy areas. Merging thermal infrared (TIR) and microwave (MW) observations is able to overcome shortcomings of single-source remote sensing to derive such an LST. However, in current merging methods, models adopted for downscaling MW LST fail to quantify the effect of temporal variation of LST. Thus, accuracy of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, we propose a new practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: annual temperature cycle component, diurnal temperature cycle component prescribed by solar geometry, and weather temperature component driven by weather change. The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over Northeast China: the resulting merged LST has an accuracy of 1.29–1.71 K when validated against in situ LST; besides, no obvious differences in accuracy of the merged LST were found between clear-sky and unclear-sky conditions. Furthermore, the proposed method outperforms the previous method in both accuracy and image quality, indicating its good capability to generate daily 1-km all-weather LST, which will benefit continuous monitoring of earth’s surface temperature.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • High-Resolution and Wide-Swath SAR Imaging via Poisson Disk Sampling and
           Iterative Shrinkage Thresholding
    • Authors: Xiaoyu Yang;Gang Li;Jinping Sun;Yu Liu;Xiang-Gen Xia;
      Pages: 4692 - 4704
      Abstract: Since the width of range swath of synthetic aperture radar (SAR) is restricted by the pulse repetition frequency, there exists a tradeoff between the azimuth resolution and the range swath width. As a result, conventional SAR imaging methods based on the Nyquist sampling theorem can hardly achieve the high resolution and wide swath simultaneously. In this paper, we propose an algorithm of high-resolution and wide-swath SAR imaging based on the combination of Poisson disk sampling and iterative shrinkage thresholding. Poisson disk sampling adopted in the azimuth direction can ensure that the interval between any two adjacent pulses is longer than the Nyquist sampling interval, which provides the potential to widen SAR imaging swath in the range direction. The imaging formation is carried out by performing the inverse operator of the chirp scaling algorithm and the shrinkage thresholding in an iterative fashion. Compared with the existing SAR imaging methods, the proposed method can realize high-resolution and wide-swath SAR imaging simultaneously with affordable computational cost. Simulations and experiments on real SAR data demonstrate the effectiveness of the proposed method.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Superpixel Tensor Model for Spatial–Spectral Classification of
           Remote Sensing Images
    • Authors: Yanfeng Gu;Tianzhu Liu;Jun Li;
      Pages: 4705 - 4719
      Abstract: Nowadays, many methods of spatial–spectral classification have been developed and achieved good results for classification with high-resolution remotely sensed images, especially superpixel-based methods. However, these methods generally consider a superpixel as a group of pixels instead of one entity, ignoring the spectral–spatial entirety in the third-order RSI data cube. In order to fully exploit the third-order spectral–spatial information, in this paper, we propose a superpixel-based tensor model for RSI classification, where a multiattribute superpixel tensor (MAST) model is constructed on the top of multiattribute superpixel maps based on the concept of extended morphological profiles (EMAPs). In order to manage the adaptive spatial nature of superpixels, we develop an increment strategy to augment all superpixels with filling up their own envelop rectangles including three different ways, i.e., 0 vector, mean vector of all the pixels within the superpixel, or original pixels. Then, we use CANDECOMP/PARAFAC (CP) decomposition to obtain the features of the unified dimension from the MASTs of various sizes. Especially, CP decomposition can deal with missing data, so we also got a fourth means of constructing the MAST. Finally, base kernels calculated, respectively, from the original spectral feature, EMAP features and MAST features are learned by multiple kernel learning methods, with the optimal kernel fed to a support vector machine to complete the classification task. The experiments conducted on four real RSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • An Optimized Deep Network Representation of Multimutation Differential
           Evolution and its Application in Seismic Inversion
    • Authors: Zhaoqi Gao;Zhibin Pan;Chen Zuo;Jinghuai Gao;Zongben Xu;
      Pages: 4720 - 4734
      Abstract: Seismic inversion problems are well-known to be nonlinear and their misfit functions often involve many local minima. Global optimization methods are capable of converging to the global minimum of a misfit function, thus, they are promising in seismic inversion. As a global optimization method, multimutation differential evolution (MMDE) has been proven to be effective in solving high-dimensional seismic inversion problems. However, it is challenging to choose the optimal parameters for MMDE to achieve the best performance in seismic inversion. In this paper, we propose a new deep network based on MMDE and name it as MMDE-Net, which enables us to learn the optimal parameters by using a network training procedure rather than empirically choosing them. Benefiting from the learned parameters, MMDE-Net has advantages over MMDE in applications. Numerical examples based on synthetic and field data set clearly indicate that MMDE-Net can provide faster convergence speed and better inversion result than conventional methods in seismic inversion.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Estimating Sea Ice Concentration From SAR: Training Convolutional Neural
           Networks With Passive Microwave Data
    • Authors: Colin L. V. Cooke;K. Andrea Scott;
      Pages: 4735 - 4747
      Abstract: Historically, sea ice concentration (SIC) has been measured through the use of passive microwave sensors, as well as human interpretation of synthetic aperture radar (SAR). Although passive microwave data are processed automatically, it suffers from poor spatial resolution and the higher frequency channels are sensitive to weather conditions. Deep learning has demonstrated its ability to perform complex and accurate analysis of images; here, we apply deep learning to estimate ice concentration from SAR scenes. We developed a deep convolutional neural network (CNN) that predicts SIC from SAR, trained upon passive microwave data. The model achieves a 5.24%/7.87% error on its train and test set, respectively. To assess the real-world applicability, we performed an independent validation on 18 SAR scenes (from two distinct geographical regions), not previously seen during training or test. Comparing against human-generated ice analysis charts, we achieved an $L1$ error of 0.2059, competitive with passive microwave ( $E_{L1} = 0.1863$ ) for the Canadian Arctic Archipelago. For the Gulf of Saint Lawrence region, we achieved an $L1$ error of 0.2653, significantly better than the passive microwave result ( $E_{L1} = 0.3593$ ). By using novel techniques for model training, as well as training entirely upon passive microwave data, we present an accessible and robust method of developing similar systems for processing SAR.1 Our results suggest that with further postprocessing, CNNs are accurate and robust enough to be used for operational tasks.1Code avail-ble: https://github.com/clvcooke/Estimating-SIC-from-SAR-github.com/clvcooke/Estimating-SIC-from-SAR
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Regionally Robust High-Spatial-Resolution Aerosol Retrieval Algorithm
           for MODIS Images Over Eastern China
    • Authors: Jing Wei;Zhanqing Li;Yiran Peng;Lin Sun;Xing Yan;
      Pages: 4748 - 4757
      Abstract: Moderate resolution imaging spectroradiometer (MODIS) has been widely used in related aerosol studies because of its long data records. However, operational aerosol optical depth (AOD) products at coarse spatial resolutions limit their applications on small and medium scales. Thus, high-spatial-resolution AOD products are needed. In this paper, a regionally robust high-resolution aerosol retrieval algorithm is developed for MODIS images over Eastern China which has complex surfaces and severe air pollution. Several major challenges in aerosol retrieval are resolved including: 1) surface reflectance by correcting for the effects of surface bidirectional reflectance distribution function using the RossThick-LiSparse model; 2) aerosol models assumed by time-series data analysis with historical aerosol optical properties measurements from the Aerosol Robotic Network (AERONET) sites; and 3) cloud screening using the proposed universal dynamic threshold cloud detection algorithm. Moreover, gas (i.e., ozone and water vapor) absorption is also corrected. Finally, our AOD retrievals are compared with the newest AERONET Version 3 Level 2.0 AOD ground-based measurements, latest MODIS Collection 6.1 AOD products at 3- and 10-km resolutions, and multiangle implementation of the atmospheric correction (MAIAC) AOD product at a 1-km resolution. The results suggest that our algorithm performs well over dark vegetated and bright urban surfaces and that 78.56% of the retrievals meet the acceptable expected error of ±(0.05% + 20%) with a mean absolute error and a root-mean-square error of 0.074 and 0.125, respectively. Comparison results indicate that the newly generated 1-km AOD data set is much better than the routine MOD04 3- and 10-km dark target data sets, and slightly better than the 10-km deep blue (with lower resolution) and 1-km MAIAC (with narrower space cov-rage) AOD products. This attests to the robustness of our algorithm that generates an AOD product with a more continuous coverage and finer resolution over complex surfaces.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a
           Concentration in Inland Waters From Remotely Sensed Multispectral and
           Hyperspectral Imagery
    • Authors: Min Xu;Hongxing Liu;Richard Beck;John Lekki;Bo Yang;Song Shu;Yang Liu;Teresa Benko;Robert Anderson;Roger Tokars;Richard Johansen;Erich Emery;Molly Reif;
      Pages: 4758 - 4774
      Abstract: Various empirical algorithms have been developed to retrieve chlorophyll-a (Chl-a) from multispectral and hyperspectral images as a proxy variable for algal blooms in inland waters. In most previous studies, a single empirical model (global model) was calibrated for the entire water body under study. Our analysis shows that the performance of a global model is limited for optically complex inland waters. We discovered that the global model tends to overestimate in some regions and underestimate in other regions, and that the model residuals (errors) display an apparent spatial autocorrelation pattern. To address the inadequacy of the global empirical model, this paper presents regionally or locally adaptive models to better estimate Chl-a concentrations for the first time. We collected two dense sets of Chl-a measurements over Harsha Lake in Ohio during a Sentinel-2A satellite overpass and a dedicated airborne hyperspectral flight. Based on the atmospherically corrected multispectral and hyperspectral images and concurrent in situ measurements, we implemented and evaluated the performance of regionally and locally adaptive models in comparison with the single global model. Among a number of candidate empirical algorithms, the two-band algorithm produces the best global model for Chl-a retrievals for both the multispectral and hyperspectral image sources. By subdividing the water body under investigation into several regions or a set of local areas, we demonstrate that regionally and locally adaptive models can improve Chl-a estimate accuracy by 13%–28% for the multispectral image and by 33%–47% for the hyperspectral image, in comparison with the best global Chl-a model.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Hyperspectral Classification Through Unmixing Abundance Maps Addressing
           Spectral Variability
    • Authors: Edurne Ibarrola-Ulzurrun;Lucas Drumetz;Javier Marcello;Consuelo Gonzalo-Martín;Jocelyn Chanussot;
      Pages: 4775 - 4788
      Abstract: Climate change and anthropogenic pressure are causing an indisputable decline in biodiversity; therefore, the need of environmental knowledge is important to develop the appropriate management plans. In this context, remote sensing and, specifically, hyperspectral imagery (HSI) can contribute to the generation of vegetation maps for ecosystem monitoring. To properly obtain such information and to address the mixed pixels inconvenience, the richness of the hyperspectral data allows the application of unmixing techniques. In this sense, a problem found by the traditional linear mixing model (LMM), a fully constrained least squared unmixing (FCLSU), is the lack of ability to account for spectral variability. This paper focuses on assessing the performance of different spectral unmixing models depending on the quality and quantity of endmembers. A complex mountainous ecosystem with high spectral changes was selected. Specifically, FCLSU and 3 approaches, which consider the spectral variability, were studied: scaled constrained least squares unmixing (SCLSU), Extended LMM (ELMM) and Robust ELMM (RELMM). The analysis includes two study cases: 1) robust endmembers and 2) nonrobust endmembers. Performances were computed using the reconstructed root-mean-square error (RMSE) and classification maps taking the abundances maps as inputs. It was demonstrated that advanced unmixing techniques are needed to address the spectral variability to get accurate abundances estimations. RELMM obtained excellent RMSE values and accurate classification maps with very little knowledge of the scene and minimum effort in the selection of endmembers, avoiding the curse of dimensionality problem found in HSI.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • An Advanced Outlier Detected Total Least-Squares Algorithm for 3-D Point
           Clouds Registration
    • Authors: Jie Yu;Yi Lin;Bin Wang;Qin Ye;Jianqing Cai;
      Pages: 4789 - 4798
      Abstract: The registration of 3-D point clouds is an important procedure during the terrestrial laser scanning data processing. Recently, due to their high flexibility and the powerful mathematical model, a large amount of least-squares-based (LSs-based) methods are proposed and widely applied to estimate the transformation parameters of 3-D point clouds registration. In these LSs-based methods some based on the generalized Gauss–Markov model do not correct the influence of random errors on source 3-D point clouds. Although there are other methods based on the errors-in-variables (EIV) model, they are inapplicable for transformation problems with large rotation angles and arbitrary scale ratio. In addition, the gross errors are usually ignored in previous studies on 3-D point clouds registration, which, however, exists commonly and could distort the registration severely. Aiming to avoid the influence of gross errors and extend its application, an advanced outlier detected total least-squares (OD-TLS) method is proposed in this paper. Based on the generalized EIV model OD-TLS performs a seven-parameter 3-D similarity transformation with large rotation angles and arbitrary scale ratio. The random errors of both source and target 3-D point clouds are considered. Furthermore, outliers are detected and removed automatically by combining the data snooping method with total least-squares (TLS) estimation. In order to indicate the benefits of OD-TLS, comparative experiments with the LS3D and weighted total least squares (WTLS) on synthetic and real-world scanned 3-D point clouds were performed. The experimental results show OD-TLS not only enhances the registration accuracy but also increases its robustness.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Deep Scene Representation for Aerial Scene Classification
    • Authors: Xiangtao Zheng;Yuan Yuan;Xiaoqiang Lu;
      Pages: 4799 - 4809
      Abstract: As a fundamental problem in earth observation, aerial scene classification tries to assign a specific semantic label to an aerial image. In recent years, the deep convolutional neural networks (CNNs) have shown advanced performances in aerial scene classification. The successful pretrained CNNs can be transferable to aerial images. However, global CNN activations may lack geometric invariance and, therefore, limit the improvement of aerial scene classification. To address this problem, this paper proposes a deep scene representation to achieve the invariance of CNN features and further enhance the discriminative power. The proposed method: 1) extracts CNN activations from the last convolutional layer of pretrained CNN; 2) performs multiscale pooling (MSP) on these activations; and 3) builds a holistic representation by the Fisher vector method. MSP is a simple and effective multiscale strategy, which enriches multiscale spatial information in affordable computational time. The proposed representation is particularly suited at aerial scenes and consistently outperforms global CNN activations without requiring feature adaptation. Extensive experiments on five aerial scene data sets indicate that the proposed method, even with a simple linear classifier, can achieve the state-of-the-art performance.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Similarity Constrained Convex Nonnegative Matrix Factorization for
           Hyperspectral Anomaly Detection
    • Authors: Wuxia Zhang;Xiaoqiang Lu;Xuelong Li;
      Pages: 4810 - 4822
      Abstract: Hyperspectral anomaly detection is very important in the remote sensing domain. The representation-based anomaly method is one of the most important hyperspectral anomaly detection methods, which uses reconstruction errors (REs) to detect anomalies. REs are affected by the basis matrix and its corresponding coefficient matrix. Mixed pixels exist because of the low-spatial resolution of hyperspectral images. The RE is not large enough to correctly distinguish the pixel difficult to classify when the basis matrix is composed of pixels. Moreover, its corresponding coefficients cannot indicate whether pixels are pure or mixed and the abundances of mixed pixels. To address the above-mentioned problems, endmembers referring to pure or relatively pure spectral signatures are explored to build the basis matrix. The RE based on the basis matrix of endmembers is much larger for the anomalous pixel difficult to correctly classify. Furthermore, its corresponding coefficient matrix of endmembers has physical meanings. Hence, a novel hyperspectral anomaly detection based on similarity constrained convex nonnegative matrix factorization is proposed from the perspective of endmembers for the first time. First, convex nonnegative matrix factorization (CNMF) is employed to obtain endmembers of background. Then, CNMF is constrained by the similarity regularization that considers different contributions of endmembers to the pixel under test to acquire the more accurate and meaningful coefficient matrix. Finally, anomalies are detected by calculating REs. The proposed algorithm is verified on both simulated and real data sets. Experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral
           Image Classification
    • Authors: Peicheng Zhou;Junwei Han;Gong Cheng;Baochang Zhang;
      Pages: 4823 - 4833
      Abstract: As one of the fundamental research topics in remote sensing image analysis, hyperspectral image (HSI) classification has been extensively studied so far. However, how to discriminatively learn a low-dimensional feature space, in which the mapped features have small within-class scatter and big between-class separation, is still a challenging problem. To address this issue, this paper proposes an effective framework, named compact and discriminative stacked autoencoder (CDSAE), for HSI classification. The proposed CDSAE framework comprises two stages with different optimization objectives, which can learn discriminative low-dimensional feature mappings and train an effective classifier progressively. First, we impose a local Fisher discriminant regularization on each hidden layer of stacked autoencoder (SAE) to train discriminative SAE (DSAE) by minimizing reconstruction error. This stage can learn feature mappings, in which the pixels from the same land-cover class are mapped as nearly as possible and the pixels from different land-cover categories are separated by a large margin. Second, we learn an effective classifier and meanwhile update DSAE with a local Fisher discriminant regularization being embedded on the top of feature representations. Moreover, to learn a compact DSAE with as small number of hidden neurons as possible, we impose a diversity regularization on the hidden neurons of DSAE to balance the feature dimensionality and the feature representation capability. The experimental results on three widely-used HSI data sets and comprehensive comparisons with existing methods demonstrate that our proposed method is effective.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Novel Two-Step Registration Method for Remote Sensing Images Based on
           Deep and Local Features
    • Authors: Wenping Ma;Jun Zhang;Yue Wu;Licheng Jiao;Hao Zhu;Wei Zhao;
      Pages: 4834 - 4843
      Abstract: Automatic remote sensing image registration has achieved great accomplishment. However, it is still a vital challenging problem to develop a robust and accurate registration method due to the negative effects of noise and imaging differences between images. For these images, it is difficult to guarantee the accuracy and robustness at the same time for one-step registration methods. To address this issue, we introduce an effective coarse-to-fine strategy and develop a new two-step registration method based on deep and local features in this paper. The first step is to calculate the approximate spatial relationship, which is obtained by a convolutional neural network. This step makes full use of the deep features to match and can generate stable results. For the second step, a matching strategy considering spatial relationship is applied to the local feature-based method. In addition, this step adopts more accurate features in location to adjust the results of the previous step. A variety of homologous and multimodal remote sensing images, including optical, synthetic aperture radar, and general map images, are used to evaluate the proposed method. The comparison experiments demonstrate that our method can apparently increase the correct correspondences, can improve the ratio of correct correspondences, and is highly robust and accurate.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Nonlinear Hyperspectral Unmixing With Graphical Models
    • Authors: Rob Heylen;Vera Andrejchenko;Zohreh Zahiri;Mario Parente;Paul Scheunders;
      Pages: 4844 - 4856
      Abstract: In optical remote sensing, phenomena such as multiple scattering, shadowing, and spatial neighbor effects generate spectral reflectances that are nonlinear mixtures of the reflectances of the surface materials. Using hyperspectral images, the obtained spectral reflectances can be unmixed. We present a general method for creating nonlinear mixing models, based on a ray-based approximation of light and a graph-based description of the optical interactions. This results in a stochastic process which can be used to calculate path probabilities and contributions, and their weighted sum. In many cases, a closed-form equation can be obtained. We illustrate the approach by deriving several existing mixing models, such as linear, bilinear, and multilinear mixing (MLM) models popular in remote sensing, layered models for vegetation canopies, and intimate mineral mixtures. Furthermore, we use the proposed technique to derive a new mixing model, which extends the MLM model with shadowing. Experiments on artificial and real data show the positive traits of this model, which also demonstrates the power of the graphical model approach.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Joint Sparse Aperture ISAR Autofocusing and Scaling via Modified Newton
           Method-Based Variational Bayesian Inference
    • Authors: Shuanghui Zhang;Yongxiang Liu;Xiang Li;Guoan Bi;
      Pages: 4857 - 4869
      Abstract: For sparse aperture (SA) radar echoes, the coherence between the undersampled pulses is destroyed, which challenges the effectiveness of the traditional autofocusing and scaling in inverse synthetic aperture radar (ISAR) imaging. A novel Bayesian ISAR autofocusing and scaling algorithm for sparse aperture is proposed, which utilizes Laplacian scale mixture, as the sparse prior of ISAR image, and variational Bayesian inference based on the Laplacian approximation to derive its posterior. In addition, it learns the phase error, rotational velocity, and center of target from radar echo automatically during the reconstruction of ISAR image, so as to achieve ISAR autofocusing and scaling for SA. Because the parameters learning is not easy to converge with the undersampled data, a modified Newton method based on joint constraint of entropy and sparsity is proposed to guarantee fast convergence in a right direction. Experimental results based on both simulated and measured data validate the robustness of the proposed ISAR imaging algorithm against SA and noise.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Heavy Metal Contamination Index Using Spectral Variables for White
           Precipitates Induced by Acid Mine Drainage: A Case Study of Soro Creek,
           South Korea
    • Authors: Jeonghwa Lim;Jaehyung Yu;Lei Wang;Yongsik Jeong;Ji Hye Shin;
      Pages: 4870 - 4888
      Abstract: We analyzed heavy metal contamination, mineral composition, and spectral characteristics of white aluminum precipitates from an acid mine drainage in Taebaek, South Korea. We introduced a single index for prediction of overall heavy metal contamination level in white precipitates using spectral variables. The white precipitates were severely contaminated with heavy metal elements and consisted of primary and secondary minerals. Due to the contamination in the environment, the precipitation pH values ranged from 4.76 to 7.80. The spectral characteristics of white precipitates are dominated by secondary minerals. The spectral reflectance of white precipitates decreased in all wavelengths, and the absorption depth related with OH, H2O, and Al-OH decreased with increase in heavy metal contamination. We found the distinctive differences at ferric iron and Al-OH absorptions between the white and reddish-brown precipitates. Based on these observations, we developed heavy metal contamination index and built prediction models using the index. The validation tests showed the index, and the regression model can accurately predict the amount of heavy metal from the spectral readings. Given the fact that the stream precipitates resulted by acid mine drainage is typically similar for each type, we expect that the heavy metal contamination index can be used to make reliable estimates of heavy metal contamination in the white precipitates by remote sensing applications.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Closed-Form Expression of Soil Temperature Sensing Depth at L-Band
    • Authors: Shaoning Lv;Yijian Zeng;Zhongbo Su;Jun Wen;
      Pages: 4889 - 4897
      Abstract: L-band passive microwave remote sensing is one of the most effective methods to map the global soil moisture distribution, yet, at which soil depth satellites are measuring is still inconclusive. Recently, with the Lv’s multilayer soil effective temperature scheme, such depth information can be revealed in the framework of the zeroth-order incoherent model when soil temperature varies linearly with soil optical depth. In this paper, we examine the relationships between soil temperature microwave sensing depth, penetration depth, and soil effective temperature, considering the nonlinear case. The soil temperature sensing depth often also named penetration depth is redefined as the depth where soil temperature equals the soil effective temperature. A method is developed to estimate soil temperature sensing depth from one pair of soil temperature and moisture measurement at an arbitrary depth, the soil surface temperature, and the deep soil temperature which is assumed to be constant in time. The method can be used to estimate the soil effective temperature and soil temperature sensing depth.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Fine-Scale SAR Soil Moisture Estimation in the Subarctic Tundra
    • Authors: Simon Zwieback;Aaron A. Berg;
      Pages: 4898 - 4912
      Abstract: In the subarctic tundra, soil moisture information can benefit permafrost monitoring and ecological studies, but fine-scale remote-sensing approaches are lacking. We explore the suitability of C-band SAR, paying attention to two challenges soil moisture retrieval faces. First, the microtopography and the heterogeneous organic soils impart unique microwave scattering properties, even in absence of noteworthy shrub cover. Empirically, we find the polarimetric response is highly random (entropies >0.7). The randomness limits the applicability of purely polarimetric approaches to soil moisture estimation, as it causes a tailor-made decomposition to break down. For comparison, the L-band scattering response is more surfacelike, also in terms of its angular characteristics. The second challenge concerns the large spatial but small temporal variability of soil moisture observed at our site. Accordingly, the Radarsat-2 C-band backscatter has a limited dynamic range (~2 dB). However, contrary to polarimetric indicators, it shows a clear surface soil moisture signal. To account for the small dynamic range while retaining a 100-m spatial resolution, we embed an empirical time-series model in a Bayesian framework. This framework adaptively pools information from neighboring grid cells, thus increasing the precision. The retrieved soil moisture index achieves correlations of 0.3–0.5 with in situ data at 5 cm depth and, upon calibration, root-mean-square errors of
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Novel Change Detection Method for Multitemporal Hyperspectral Images
           Based on Binary Hyperspectral Change Vectors
    • Authors: Daniele Marinelli;Francesca Bovolo;Lorenzo Bruzzone;
      Pages: 4913 - 4928
      Abstract: Hyperspectral (HS) images provide a dense sampling of target spectral signatures. Thus, they can be used in a multitemporal framework to detect and discriminate between different kinds of fine spectral change effectively. However, due to the complexity of the problem and the limited amount of multitemporal images and reference data, only a few works in the literature addressed change detection (CD) in HS images. In this paper, we present a novel method for unsupervised multiple CD in multitemporal HS images based on a discrete representation of the change information. Differently from the state-of-the-art methods, which address the high dimensionality of the data using band reduction or selection techniques, in this paper, we focus our attention on the representation and exploitation of the change information present in each band. After a band-by-band pixel-based subtraction of the multitemporal images, we define the hyperspectral change vectors (HCVs). The change information in the HCVs is then simplified. To this end, the radiometric information of each band is separately analyzed to generate a quantized discrete representation of the HCVs. This discrete representation is explored by considering the hierarchical nature of the changes in HS images. A tree representation is defined and used to discriminate between different kinds of change. The proposed method has been tested on a simulated data set and two real multitemporal data sets acquired by the Hyperion sensor over agricultural areas. Experimental results confirm that the discrete representation of the change information is effective when used for unsupervised CD in multitemporal HS data.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Multisource Region Attention Network for Fine-Grained Object Recognition
           in Remote Sensing Imagery
    • Authors: Gencer Sumbul;Ramazan Gokberk Cinbis;Selim Aksoy;
      Pages: 4929 - 4937
      Abstract: Fine-grained object recognition concerns the identification of the type of an object among a large number of closely related subcategories. Multisource data analysis that aims to leverage the complementary spectral, spatial, and structural information embedded in different sources is a promising direction toward solving the fine-grained recognition problem that involves low between-class variance, small training set sizes for rare classes, and class imbalance. However, the common assumption of coregistered sources may not hold at the pixel level for small objects of interest. We present a novel methodology that aims to simultaneously learn the alignment of multisource data and the classification model in a unified framework. The proposed method involves a multisource region attention network that computes per-source feature representations, assigns attention scores to candidate regions sampled around the expected object locations by using these representations, and classifies the objects by using an attention-driven multisource representation that combines the feature representations and the attention scores from all sources. All components of the model are realized using deep neural networks and are learned in an end-to-end fashion. Experiments using RGB, multispectral, and LiDAR elevation data for classification of street trees showed that our approach achieved 64.2% and 47.3% accuracies for the 18-class and 40-class settings, respectively, which correspond to 13% and 14.3% improvement relative to the commonly used feature concatenation approach from multiple sources.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • PAN-Guided Cross-Resolution Projection for Local Adaptive Sparse
           Representation- Based Pansharpening
    • Authors: Haitao Yin;
      Pages: 4938 - 4950
      Abstract: Sparse representation (SR)-based methods solve pansharpening as an image superresolution problem and receive great popularity. Conventional approaches assume that the high- and low-resolution images have the same sparse coefficients. However, the identity mapping is not universal and also limits the performance. To overcome this limitation, this paper proposes a PAN-guided cross-resolution projection-based pan-sharpening (PGCP-PS) which incorporates the SR image superresolution and details injection pansharpening scheme into a framework. The basic idea of PGCP-PS is to inject a possible offset into the SR superresolution reconstructed part. In addition, the same sparse coefficients assumption across different resolutions is relaxed as the same sparse support with a local adaptive cross-resolution projection. By exploiting the similarity between panchromatic (PAN) and multispectral (MS) images, the cross-resolution projection and offset for sharpening the MS image are estimated from a simulated PAN image superresolution scenario. The high- and low-resolution dictionaries used in the stage of SR image superresolution are learned from PAN image and its degraded version. A series of experimental results on the reduced-scale and full-scale data sets demonstrates that the PGCP-PS outperforms some advanced methods and existing SR-based methods.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Spatial–Temporal Super-Resolution Land Cover Mapping With a Local
           Spatial–Temporal Dependence Model
    • Authors: Xiaodong Li;Feng Ling;Giles M. Foody;Yong Ge;Yihang Zhang;Lihui Wang;Lingfei Shi;Xinyan Li;Yun Du;
      Pages: 4951 - 4966
      Abstract: The mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial–temporal SRM (STSRM) extends the basic SRM to include a temporal dimension by using a finer-spatial resolution land cover map that pre- or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time-series data. In addition, the STSRM methods define the temporal dependence globally, and neglect the spatial variation of land cover temporal dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local temporal dependence model, in which the temporal dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A New Method to Enhance the Spatial Features of Multitemporal NDVI Image
           Series
    • Authors: Fabio Maselli;Marta Chiesi;Maurizio Pieri;
      Pages: 4967 - 4979
      Abstract: A novel spatiotemporal fusion (STF) method is presented to enhance the spatial features of low-spatial-resolution (LR) normalized difference vegetation index (NDVI) image series based on single-date high-spatial-resolution (HR) imagery. The method is particularly suitable for areas where the main vegetation types show asynchronous NDVI evolutions whose spatial distribution cannot be properly characterized by a single-date HR image. In contrast with previous STF methods, the new algorithm identifies these vegetation types by automatically decomposing the LR multitemporal data series, which offers a complete description of major seasonal NDVI evolutions. The new method, named Spatial Enhancer of Vegetation Index image Series (SEVIS), is tested in two Italian study areas using annual MODIS NDVI data sets and some Landsat 8 OLI images taken in different seasons. The performances of SEVIS are analyzed in comparison with those of two other STF methods, the classical Spatial and Temporal Adaptive Fusion Model (STARFM) and the more recent Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm. The results obtained indicate that the three methods perform differently depending mainly on the synchronicity of the NDVI evolutions from the base to the prediction dates. Specifically, SEVIS outperforms the other two methods when the NDVI values evolve differently during the prediction period, i.e., when the base and prediction images are poorly correlated.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Fuzziness Modeling of Polarized Scattering Mechanisms and PolSAR Image
           Classification Using Fuzzy Triplet Discriminative Random Fields
    • Authors: Wanying Song;Ming Li;Peng Zhang;Yan Wu;
      Pages: 4980 - 4993
      Abstract: Dominant scattering mechanism (DSM) obtained by Freeman decomposition is significant for polarimetric synthetic aperture radar (PolSAR) image classification. To preserve the purity of scattering characteristics, it restricts pixels in a scattering category to be classified with other pixels in the same scattering category. However, due to the speckle and the limited image resolution, it is difficult to obtain the DSMs of some pixels, which are defined as the fuzziness of polarized scattering mechanisms. Therefore, we first consider a particular—and pertinent—auxiliary field, and then propose the fuzzy triplet discriminative random fields (FTDF) model to describe the fuzziness of polarized scattering mechanisms, thus categorizing the scattering mechanisms into four classes: surface scattering, double-bounce scattering, volume scattering, and mixed scattering. The pixels in the first three categories are with specific DSMs, and the FTDF model introduces an exponential kernel distance to combine the multiple features of PolSAR data into classification. For the pixels in the mixed scattering, FTDF introduces a fuzzy clustering algorithm regularized by Kullback–Leibler information to consider the fuzzy DSMs, thus enhancing the classification. Then the fuzziness modeling of polarized scattering mechanisms can guide the classification of PolSAR images. The experimental results on real PolSAR images demonstrate the effectiveness of the FTDF model, and illustrate that it can improve the classification accuracy, and simultaneously preserve the purity of scattering mechanisms.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Mapping the Irradiance Field of a Single Tree: Quantifying
           Vegetation-Induced Adjacency Effects
    • Authors: Daniel Kükenbrink;Andreas Hueni;Fabian D. Schneider;Alexander Damm;Jean-Philippe Gastellu-Etchegorry;Michael E. Schaepman;Felix Morsdorf;
      Pages: 4994 - 5011
      Abstract: Imaging spectroscopy is frequently used to assess traits and functioning of vegetated ecosystems. Applied reflectance- and radiance-based approaches critically rely on accurate estimates of surface irradiance. Accurate retrievals of surface irradiance are, however, nontrivial and often error-prone, thus causing inaccurate estimates of vegetation information. We analyze the irradiance field surrounding an isolated tree using the 3-D radiative transfer model DART in high spatial (25 cm) and spectral (1 nm, 350–2500 nm) resolution. We validate modeled irradiance with in situ measurements and quantify the impact of erroneous surface irradiance estimates on the retrieval of vegetation indices. We observe the irradiance gradients in the cast shadows of
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Geographically and Temporally Weighted Regression Model for Spatial
           Downscaling of MODIS Land Surface Temperatures Over Urban Heterogeneous
           Regions
    • Authors: Yidong Peng;Weisheng Li;Xiaobo Luo;Hua Li;
      Pages: 5012 - 5027
      Abstract: The fine spatial resolution (~100 m) land surface temperature (LST) is a key variable of great concern in various environmental studies over urban heterogeneous regions. An improvement in the spatial resolution of the coarse spatial resolution LST is an effective way to extend its potential uses in applications that have strict requests on both the spatial and temporal resolutions. However, previous statistical downscaling algorithms were proposed mainly by addressing the spatial variability in the LST while neglecting the temporal variability. In this paper, we propose a new algorithm based on a geographically and temporally weighted regression (GTWR) model for spatial downscaling of the Moderate Resolution Imaging Spectroradiometer LST data from 1000 to 100 m. The GTWR-based algorithm with temporally and geographically varying regression coefficients can capture both the spatial and temporal variabilities in the LST from the time series data at a coarse spatial resolution for effectively reconstructing the subpixel variability in the LST at fine spatial resolution. In addition, because of a better ability to explain the LST variability over urban heterogeneous regions, a normalized difference built-up index and a digital elevation model were selected as auxiliary variables. Taking Beijing and Lanzhou as examples, the performance of the GTWR-based algorithm was assessed by comparing the results with the TsHARP and GWR-based algorithms and the Landsat-8 LST. The results indicate that the GTWR-based algorithm outperforms the above-mentioned algorithms with lower mean root mean square error (1.62 °C) and mean absolute error (1.28 °C) and better agreement between the GTWR downscaled LST and the Landsat-8 LST.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • R3-Net: A Deep Network for Multioriented Vehicle Detection in
           Aerial Images and Videos
    • Authors: Qingpeng Li;Lichao Mou;Qizhi Xu;Yun Zhang;Xiao Xiang Zhu;
      Pages: 5028 - 5042
      Abstract: Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R3-Net), to detect multioriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable position-sensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Spectral Image Fusion From Compressive Measurements Using Spectral
           Unmixing and a Sparse Representation of Abundance Maps
    • Authors: Edwin Vargas;Henry Arguello;Jean-Yves Tourneret;
      Pages: 5043 - 5053
      Abstract: In the past years, one common way of enhancing the spatial resolution of a hyperspectral (HS) image has been to fuse it with complementary information coming from multispectral (MS) or panchromatic images. This paper proposes a new method for reconstructing a high-spatial, high-spectral image from measurements acquired after compressed sensing by multiple sensors of different spectral ranges and spatial resolutions, with specific attention to HS and MS compressed images. To solve this problem, we introduce a fusion model based on the linear spectral unmixing model classically used for HS images and investigate an optimization algorithm based on a block coordinate descent strategy. The nonnegative and sum-to-one constraints resulting from the intrinsic physical properties of abundances as well as a total variation penalization are used to regularize this ill-posed inverse problem. Simulation results conducted on realistic compressed HS and MS images show that the proposed algorithm can provide fusion results that are very close to those obtained with uncompressed images, with the advantage of using a significantly reduced number of measurements.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Performance Analysis of Parameter Estimation in Electromagnetic Induction
           Data
    • Authors: Andrew J. Kerr;Waymond R. Scott;James H. Mcclellan;
      Pages: 5054 - 5066
      Abstract: We derive the Cramer–Rao lower bound (CRB) for target tensor amplitudes and target location parameters that can be estimated from electromagnetic induction (EMI) measurements of a target. In deriving the bound, no restrictions are placed on the target type, target orientation, quantity, or location of the measurement positions, nor the geometry of the EMI sensor or sensor array. The analysis is applicable to both a scanned sensor and a stationary array, as well as both time- and frequency-domain sensors. We show how the bound varies as a function of target type, orientation, depth, and signal-to-noise ratio. In addition, we illustrate the ways to use the CRB and the Fisher information matrix to analyze the relationships between the tensor amplitudes and location parameters. We show results of the CRB analysis applied to an experimental frequency-domain Georgia Tech sensor for a few target types for a nominal 2-D scan geometry as well as the Time-domain Electromagnetic Multi-sensor Towed Array Detection System. We also provide an algorithm for estimating the target location and tensor that achieves the CRB.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Determination of Optimum Tie Point Interval for SAR Image Coregistration
           by Decomposing Autocorrelation Coefficient
    • Authors: Weibao Zou;Libin Chen;
      Pages: 5067 - 5084
      Abstract: Interferometric synthetic aperture radar (InSAR) is a well-established technique for the generation of digital elevation models and/or the measurement of ground surface deformations. In InSAR data processing, the first step is the image coregistration achieved by using a set of tie points which are the conjugate image points on both master and slave images. To achieve reliable coregistration, a set of points is used instead of a single point. The interval of tie points, i.e., tie point density, will greatly affect the reliability of the coregistration. Hitherto, there have been no effective methods for the determination of optimum tie point interval for the purpose. In practice, this parameter is determined by experience. In this paper, a method for determination of optimum tie point interval is presented based on theoretical analysis of autocorrelation coefficient of SAR image, which reflects the similarity between image pixels. After computation of autocorrelation coefficient in range and azimuth, it is decomposed into low and high frequencies by a wavelet transform. Then, by a combined analysis of the variations of wave amplitudes with distance, the optimum interval for tie point matching can be determined. It is found that the optimum tie point interval in azimuth and range can be different. Two pairs of SAR images acquired by different platforms are used to validate the proposed method. An optimum tie point interval can be determined in this way for any pair of SAR images.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Spatial Density Peak Clustering for Hyperspectral Image Classification
           With Noisy Labels
    • Authors: Bing Tu;Xiaofei Zhang;Xudong Kang;Jinping Wang;Jón Atli Benediktsson;
      Pages: 5085 - 5097
      Abstract: The “noisy label” problem is one of the major challenges in hyperspectral image (HSI) classification. In order to address this problem, a spatial density peak (SDP) clustering-based method is proposed to detect mislabeled samples in the training set. Specifically, the proposed methods consist of the following steps: first, the correlation coefficients among the training samples in each class are estimated. In this step, instead of measuring the correlation coefficients by considering individual samples, all neighbor samples or $K$ representative neighbor samples in a local window surrounding each training sample are considered. By this way, the spatial contextual information could be used, and two versions of the proposed method, i.e., measuring the correlation coefficients using all neighbor samples or $K$ representative samples, are referred as SDP and $K$ -SDP, respectively. Second, with the correlation coefficients calculated above, the local density of each training sample can be obtained by the DP clustering algorithm. Finally, those mislabeled samples which usually have lower local densities in each class are able to be identified by a defined decision function. The effectiveness of the proposed detection method is evaluated using a series of spectral and spectral-spatial classification methods on several real hyperspectral data sets.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • A Unified Formulation of Polarimetric Weather Radar With Application to IQ
           Data Simulation
    • Authors: Verónica Santalla del Río;María Vera-Isasa;
      Pages: 5098 - 5107
      Abstract: The meteorological parameters derived from polarimetric weather radar measurements are of interest for nowcasting and to feed numerical models for forecasting. The accuracy of the derived meteorological parameters is affected by the polarimetric measurement scheme considered as well as by the system hardware (e.g., the antenna radiation patterns or receive channels imbalances). However, determining the accuracy of the estimated meteorological parameters is difficult since no ground truth is available. This fact has motivated the development of weather radar data simulators that allow comparison of the assumed weather parameters used to feed the system and the finally estimated weather parameters. In this paper, the weather radar model is reviewed. A unified formulation applicable to all polarimetric measurement systems is developed. This formulation shows that antenna effects can be decoupled from scattering effects. Based on this formulation, a weather radar simulator is proposed. This radar simulator does not require the fine sampling in the elevation and azimuth directions required by previous simulators. Therefore, the computational load and execution times are significantly reduced. Finally, it is important to point out that the radar simulator proposed provides as much fidelity to the actual radar system as previous simulators.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Joint Multiscale Direct Envelope Inversion of Phase and Amplitude in the
           Time–Frequency Domain
    • Authors: Yong Hu;Ru-Shan Wu;Li-Guo Han;Pan Zhang;
      Pages: 5108 - 5120
      Abstract: Time–frequency analysis can reveal local variations and allow for separation of phase and amplitude information of nonstationary seismic waveforms. Seismic signals are used since long as a robust tool for inversion of underground structures, as has been the practice in geophysical exploration. However, the mixing of phase and amplitude in seismic data increases the nonlinearity of seismic inversion. The authors first use Gabor transform to separate the phase and amplitude information of envelope data, and then introduce an adaptive factor into the misfit function to redistribute the weight of phase and amplitude information for direct envelope inversion (DEI) in the time–frequency domain. By adopting this procedure, greater flexibility can be achieved in operating the local phase of envelope and waveform spectra to enhance stability of multiscale phase inversion. For DEI, the direct envelope Fréchet derivative is used, and thus, no weak scattering assumption is imposed on the joint multiscale DEI of phase and amplitude (PADEI). Compared with the DEI method, the PADEI can better recover the deeper parts of salt-bottom and subsalt structures by boosting the signal energy and weakening the nonlinearity of the waveform inversion.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Random Walks for Pansharpening in Complex Tight Framelet Domain
    • Authors: Jingkai Wang;Xiaoyuan Yang;Ridong Zhu;
      Pages: 5121 - 5134
      Abstract: In this paper, a new random walk (RW) pansharpening method on the basis of the complex framelet domain is proposed. In the process of fusion, the hidden Markov tree model is first established based on the statistical properties of complex high-pass framelet coefficients. On this basis, a novel RW fusion algorithm is presented. Then, the probabilities of complex framelet coefficients being allotted original images are solved by the linear system of equations. Based on these probabilities, the spatial details of the panchromatic image are selectively injected into the multispectral (MS) image to get a space-enhanced MS image. In the end, the GeoGye-1, WorldView-3, and WorldView-2 remote sensing image data sets are used to evaluate the performance of the presented method quantitatively and qualitatively. The results of the experiment show that our method outperforms some state-of-the-art approaches. It can improve the spatial resolution of the MS image while keeping the spectral information.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Vicarious Calibration of Orbiting Carbon Observatory-2
    • Authors: Carol J. Bruegge;David Crisp;Mark C. Helmlinger;Fumie Kataoka;Akihiko Kuze;Richard A. Lee;James L. McDuffie;Robert A. Rosenberg;Florian Max Schwandner;Kei Shiomi;Shanshan Yu;
      Pages: 5135 - 5145
      Abstract: Vicarious calibration methods use well-characterized surface sites to complement other on-orbit radiometric calibration techniques. Since 2009, NASA’s Orbiting Carbon Observatory-2 (OCO-2) and Japan’s Greenhouse gasses Observing SATellite teams have conducted annual campaigns at Railroad Valley, NV, USA, for this purpose. These sensors pose special challenges due to their large footprint sizes and view angles. OCO-2 sweeps the playa surface during a targeted overpass of the test site, and records data at a number of viewing angles. The smallest of these is selected for processing, thereby minimizing the off-nadir correction. Surface reflectances at nadir are recorded by the field team, and the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product is used to provide the small, off-nadir correction. Another MODIS product, the Level 1B top-of-atmosphere radiance product, is used to validate the results and to provide input into the OCO-2 calibration uncertainty estimate. From 11 experiments, the ratio of radiances reported by the OCO-2 Level 1B data product to those from the field campaigns is 1.01, 1.04, and 1.01 for the three OCO-2 spectral bands. These analyses validate the data product absolute calibration, to within the 5% requirement. The need for executing these experiments will be of continued importance to OCO-3. This sensor has an on-board calibrator that provides a dark signal and lamps for response trends but does not have the on-board solar-diffuser present on OCO-2, and thus cannot track degradations relative to the Sun.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • ORSIm Detector: A Novel Object Detection Framework in Optical Remote
           Sensing Imagery Using Spatial-Frequency Channel Features
    • Authors: Xin Wu;Danfeng Hong;Jiaojiao Tian;Jocelyn Chanussot;Wei Li;Ran Tao;
      Pages: 5146 - 5158
      Abstract: With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-frequency channel feature (SFCF) by jointly considering the rotation-invariant channel features constructed in the frequency domain and the original spatial channel features (e.g., color channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Detecting Infrared Maritime Targets Overwhelmed in Sun Glitters by
           Antijitter Spatiotemporal Saliency
    • Authors: Bin Wang;Yuichi Motai;Lili Dong;Wenhai Xu;
      Pages: 5159 - 5173
      Abstract: When detecting infrared maritime targets on sunny days, strong sun glitters can lower the detection accuracy tremendously. To this problem, we proposed a robust antijitter spatiotemporal saliency generation with parallel binarization (ASSGPB) method. Its main contribution is to facilitate to improve the infrared maritime target detection accuracy in this situation. The ASSGPB algorithm exploits the target’s spatial saliency and temporal consistency to separate real targets from clutter areas. The ASSGPB first corrects image intensity distribution with a central inhibition difference of Gaussian filter. Then, a self-defined spatiotemporal saliency map (STSM) generator is used to generate an STSM in five consecutive frames while compensating interframe jitters by a joint block matching. Finally, a parallel binarization method is adopted to segment real targets in STSM while keeping full target areas. To evaluate the performance of ASSGPB, we captured eight different image sequences (20420 frames in total) that were significantly contaminated by strong sun glitters. The ASSGPB realized 100% detection rate and 0.45% false alarm rate in these data sets, greatly outperforming four state-of-the-art algorithms. Thus, a great applicability of ASSGPB has been verified through our experiments.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image
           Denoising
    • Authors: Jize Xue;Yongqiang Zhao;Wenzhi Liao;Jonathan Cheung-Wai Chan;
      Pages: 5174 - 5189
      Abstract: Hyperspectral image (HSI) enjoys great advantages over more traditional image types for various applications due to the extra knowledge available. For the nonideal optical and electronic devices, HSI is always corrupted by various noises, such as Gaussian noise, deadlines, and stripings. The global correlation across spectrum (GCS) and nonlocal self-similarity (NSS) over space are two important characteristics for HSI. In this paper, a nonlocal low-rank regularized CANDECOMP/PARAFAC (CP) tensor decomposition (NLR-CPTD) is proposed to fully utilize these two intrinsic priors. To make the rank estimation more accurate, a new manner of rank determination for the NLR-CPTD model is proposed. The intrinsic GCS and NSS priors can be efficiently explored under the low-rank regularized CPTD to avoid tensor rank estimation bias for denoising performance. Then, the proposed HSI denoising model is performed on tensors formed by nonlocal similar patches within an HSI. The alternating direction method of multipliers-based optimization technique is designed to solve the minimum problem. Compared with state-of-the-art methods, the proposed algorithm can greatly promote the denoising performance of an HSI in various quality assessments.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • $l_{({1}/{2})}$+ +Regularized+Joint+Sparse+and+Low-Rank+Recovery&rft.title=Geoscience+and+Remote+Sensing,+IEEE+Transactions+on&rft.issn=0196-2892&rft.date=2019&rft.volume=57&rft.spage=5190&rft.epage=5197&rft.aulast=George;&rft.aufirst=Baburaj&rft.au=Baburaj+Madathil;Sudhish+N.+George;">Simultaneous Reconstruction and Anomaly Detection of Subsampled
           Hyperspectral Images Using $l_{({1}/{2})}$ Regularized Joint Sparse and
           Low-Rank Recovery
    • Authors: Baburaj Madathil;Sudhish N. George;
      Pages: 5190 - 5197
      Abstract: This paper focuses on an unsupervised anomaly detection approach from a subsampled hyperspectral image (HSI) data. Unlike the state-of-the-art methods, the proposed method does not require the construction of a large matrix by the vectorization of spectral bands or unfolding of entire HSI data into a huge matrix. Image reconstruction and anomaly detection are performed at the same time on the subsampled HSI data to reduce the storage/transmission requirements and processing time. In the proposed framework, the HSI data are decomposed into background and anomaly by $l_{({1}/{2})}$ regularized joint sparse and low-rank decomposition. Since the spectral bands of HSI data are highly correlated, it can be effectively utilized for background modeling. Inspired by this fact, the low-dimensional structure of the background is characterized by modeling it as a combination of common low-rank and spectral-specific low-rank components. To further improve the separation between background and anomaly, the recently proposed $l_{({1}/{2})}$ regularization for the sparse component is employed. The experimental results reveal that the proposed method outperforms the existing methods on both reconstruction quality and detection performance.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Comments on “Automated Determination of Snow Water Equivalent by
           Acoustic Reflectometry”
    • Authors: Kapil Dev Tyagi;Arun Kumar;Rajendar Bahl;Karamjeet Singh;Praveen K. Srivastava;
      Pages: 5198 - 5203
      Abstract: This paper presents the issues in snow water equivalent (SWE) determination using acoustic reflectometry. To determine SWE, the density and the depth of each layer of snow in a snow pack need to be estimated. A noninvasive acoustic reflectometry technique to estimate the SWE is proposed in the mentioned paper, in which it is shown that SWE of the snowpacks with approximately 100 cm depth can be determined using a maximum length sequence (MLS) of length 7 as a probe signal. We performed similar experiments as reported in the mentioned paper to determine SWE using acoustic reflectometry but failed to replicate the given method. The signal processing-related issues such as design of probe signal, direct pickup problem, and so on of the mentioned paper are investigated and presented in this communication. We show that it is not possible to extract the reflection response by using the MLS of length 7 as a probe signal. The amount of sound attenuation and absorption in the snow medium is very high, of the order of 1 dB/cm and greater depending on the frequency, as reported in the literature and measured by us. Hence, the depth of snowpack investigated in the mentioned paper is practically difficult to achieve using conventional loudspeaker and microphone. Also, it has been explained how the cross-correlation peak width and, hence, the layer resolution achieved by the authors of the mentioned paper is not practically feasible by their reported method. We have also demonstrated that Wiener spiking deconvolution-based technique is not able to obtain the sharp peaks as shown in the mentioned paper.
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • IEEE Access
    • Pages: 5204 - 5204
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • IEEE Access
    • Pages: 5207 - 5207
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
  • Introducing IEEE Collabratec
    • Pages: 5208 - 5208
      PubDate: July 2019
      Issue No: Vol. 57, No. 7 (2019)
       
 
 
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