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Journal Cover Geoscience and Remote Sensing, IEEE Transactions on
  [SJR: 1.975]   [H-I: 168]   [158 followers]  Follow
    
   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
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • IEEE Transactions on Geoscience and Remote Sensing information for authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Super-Resolved Fine-Scale Sea Ice Motion Tracking
    • Authors: Yang Xian;Zisis I. Petrou;Yingli Tian;Walter N. Meier;
      Pages: 5427 - 5439
      Abstract: Monitoring sea ice activities is particularly critical to safe naval operations in the Arctic Ocean. Accurately tracking sea ice motions is essential to validate or even improve sea ice models for ice hazard forecasts at a fine scale. Fine-scale motions can be tracked from high-resolution radar or optical satellite imagery but with limited coverage. Daily motions over the entire Arctic are retrievable from passive microwave data, but at a much lower spatial resolution. Thus, providing motions at the passive microwave spatial and temporal coverage, but at an enhanced spatial resolution, will be a significant benefit. To break the resolution limitation and to boost tracking accuracy, a sequential super-resolved fine-scale sea ice motion tracking framework is proposed in which a hybrid example-based single image super-resolution algorithm is employed before the tracking procedure. Experiments demonstrate that the proposed framework significantly improves the tracking performance in both accuracy and robustness for a benchmark algorithm and a recently proposed state-of-the-art tracking algorithm.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • ICESAT/GLAS Altimetry Measurements: Received Signal Dynamic Range and
           Saturation Correction
    • Authors: Xiaoli Sun;James B. Abshire;Adrian A. Borsa;Helen Amanda Fricker;Donghui Yi;John P. DiMarzio;Fernando S. Paolo;Kelly M. Brunt;David J. Harding;Gregory A. Neumann;
      Pages: 5440 - 5454
      Abstract: NASA's Ice, Cloud, and land Elevation Satellite (ICESat), which operated between 2003 and 2009, made the first satellite-based global lidar measurement of earth's ice sheet elevations, sea-ice thickness, and vegetation canopy structure. The primary instrument on ICESat was the Geoscience Laser Altimeter System (GLAS), which measured the distance from the spacecraft to the earth's surface via the roundtrip travel time of individual laser pulses. GLAS utilized pulsed lasers and a direct detection receiver consisting of a silicon avalanche photodiode and a waveform digitizer. Early in the mission, the peak power of the received signal from snow and ice surfaces was found to span a wider dynamic range than anticipated, often exceeding the linear dynamic range of the GLAS 1064-nm detector assembly. The resulting saturation of the receiver distorted the recorded signal and resulted in range biases as large as ~50 cm for ice- and snow-covered surfaces. We developed a correction for this “saturation range bias” based on laboratory tests using a spare flight detector, and refined the correction by comparing GLAS elevation estimates with those derived from Global Positioning System surveys over the calibration site at the salar de Uyuni, Bolivia. Applying the saturation correction largely eliminated the range bias due to receiver saturation for affected ICESat measurements over Uyuni and significantly reduced the discrepancies at orbit crossovers located on flat regions of the Antarctic ice sheet.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Target Detection via Bayesian-Morphological Saliency in High-Resolution
           SAR Images
    • Authors: Zhaocheng Wang;Lan Du;Hongtao Su;
      Pages: 5455 - 5466
      Abstract: The classical target detection methods in synthetic aperture radar (SAR) images are mainly dependent on the intensity differences between the targets and clutter. Although they are effective in the simple scenes with high signal-to-clutter ratio (SCR), they may lose effectiveness in the complex scenes with low SCR. Generally, in high-resolution SAR images, the targets present not only high intensities but also specific size characteristics compared with the clutter. Based on this fact, in this paper, we propose a new target detection method for high-resolution SAR images via Bayesian-morphological saliency, which mainly contains two stages: Bayesian saliency map construction and morphological saliency map construction. The Bayesian saliency map can obtain the complete structures of the bright objects including the targets of interest and some bright clutter, via the superpixel segmentation and Bayesian framework. Furthermore, the morphological saliency map can highlight the targets of interest while suppressing both the natural and man-made clutter via the size prior information of the targets. The experimental results on the miniSAR real data set show that the proposed target detection method is effective.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Multitemporal SAR Image Despeckling Based on Block-Matching and
           Collaborative Filtering
    • Authors: Giovanni Chierchia;Mireille El Gheche;Giuseppe Scarpa;Luisa Verdoliva;
      Pages: 5467 - 5480
      Abstract: We propose a despeckling algorithm for multitemporal synthetic aperture radar (SAR) images based on the concepts of block-matching and collaborative filtering. It relies on the nonlocal approach, and it is the extension of SAR-BM3D for dealing with multitemporal data. The technique comprises two passes, each one performing grouping, collaborative filtering, and aggregation. In particular, the first pass performs both the spatial and temporal filtering, while the second pass only the spatial one. To avoid increasing the computational cost of the technique, we resort to lookup tables for the distance computation in the block-matching phases. The experiments show that the proposed algorithm compares favorably with respect to state-of-the-art reference techniques, with better results both on simulated speckled images and on real multitemporal SAR images.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Infrared Dim and Small Target Detection Based on Stable Multisubspace
           Learning in Heterogeneous Scene
    • Authors: Xiaoyang Wang;Zhenming Peng;Dehui Kong;Yanmin He;
      Pages: 5481 - 5493
      Abstract: Infrared (IR) dim and small target detection in a highly complex background play an important role in many applications, and remain a challenging problem. In this paper, a novel method named stable multisubspace learning is presented to deal with this problem. The new method takes into account the inner structure of actual images so that it overcomes the shortage of the traditional method. First, by analyzing the multisubspace structure of heterogeneous background data, a corresponding image model is proposed using subspace learning strategy. This model is also stable to noise interference. Second, an efficient optimization algorithm is designed to solve the proposed IR image model. By adding the proper postprocessing procedure, we can get the detection result. Experiments on simulation scenes and real scenes show that the proposed method has superior detection ability under heterogeneous background.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Comparison of Elevation Change Detection Methods From ICESat Altimetry
           Over the Greenland Ice Sheet
    • Authors: Denis Felikson;Timothy J. Urban;Brian C. Gunter;Nadège Pie;Hamish D. Pritchard;Robert Harpold;Bob E. Schutz;
      Pages: 5494 - 5505
      Abstract: Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA's Ice, Cloud, and land Elevation Satellite (ICESat) created altimetric surveys of GrIS surface elevations over the 2003-2009 operational period of the mission. This paper compares four change detection methods using Release 634 ICESat laser altimetry data: repeat tracks (RTs), crossovers (XOs), overlapping footprints (OFPs), and triangulated irregular networks (TINs). All four methods begin with a consistently edited data set and yield estimates of volumetric loss of ice from the GrIS ranging from -193 to -269 km3/yr. Using a uniform approach for quantifying uncertainties, we find that volume change rates at the drainage system scale from the four methods can be reconciled within 1-σ uncertainties in just 5 of 19 drainage systems. Ice-sheet-wide volume change estimates from the four methods cannot be reconciled within 1-σ uncertainties. Our volume change estimates lie within the range of previously published estimates, highlighting that the choice of method plays a dominant role in the scatter of volume change estimates. We find that for much of the GrIS, the OFP and TIN methods yield the lowest volume change uncertainties because of their superior spatial distribution of elevation change rate estimates. However, the RT and XO methods offer inherent advantages, and the future work to combine the elevation change detection methods to produce better estimates is warranted.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • REAPER: Reprocessing 12 Years of ERS-1 and ERS-2 Altimeters and Microwave
           Radiometer Data
    • Authors: David J. Brockley;Steven Baker;Pierre Féménias;Bernat Martínez;Franz-Heinrich Massmann;Michiel Otten;Frédéric Paul;Bruno Picard;Pierre Prandi;Mònica Roca;Sergei Rudenko;Remko Scharroo;Pieter Visser;
      Pages: 5506 - 5514
      Abstract: Twelve years (1991-2003) of ERS-1 and ERS-2 altimetry data have been reprocessed within the European Space Agency (ESA) reprocessing altimeter products for ERS (REAPER) project using an updated, modern set of algorithms and auxiliary models. The reprocessed data set (identified as RP01) has been cross-calibrated against the reprocessed ENVISAT V2.1 data. The format of this reprocessed data set is network common data form (version 3). The new data set shows a clear improvement in data quality beyond that of previous releases. The product validation shows reduction of the mean standard deviation of the sea-surface height differences from 8.1 (previously available product) to 6.7 cm (RP01). This paper presents the details of how the reprocessing was conducted and shows selected results from the validation and quality-assurance processes. The major improvements of the REAPER RP01 data set with respect to the previous ESA ERS radar altimetry (RA) products are due to the use of four ENVISAT RA-2 retrackers, RA calibration improvements, new reprocessed precise orbit solutions, ECMWF ERA-interim model for meteorological corrections, new ionospheric corrections, and new sea state. The intent of this paper is to aid the reader in understanding the benefits of the new data set for their particular use-case.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Geodetic Imaging of Time-Dependent Three-Component Surface Deformation:
           Application to Tidal-Timescale Ice Flow of Rutford Ice Stream, West
           Antarctica
    • Authors: Pietro Milillo;Brent Minchew;Mark Simons;Piyush Agram;Bryan Riel;
      Pages: 5515 - 5524
      Abstract: We present a method for inferring time-dependent three-component surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. Displacements are parameterized in time with a dictionary of displacement functions. The algorithm extends an earlier single-component (i.e., single line of sight) framework for time-series analysis to three spatial dimensions using combinations of multitemporal, multigeometry interferometic synthetic aperture radar (InSAR) and/or pixel offset (PO) maps. We demonstrate this method with a set of 101 pairs of azimuth and range PO maps generated for a portion of the Rutford Ice Stream, West Antarctica, derived from data collected by the COSMO-SkyMed satellite constellation. We compare our results with previously published InSAR mean velocity fields and selected GPS time series and show that our resulting three-component surface displacements resolve both secular motion and tidal variability.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Scene Classification Based on the Fully Sparse Semantic Topic Model
    • Authors: Qiqi Zhu;Yanfei Zhong;Liangpei Zhang;Deren Li;
      Pages: 5525 - 5538
      Abstract: In high spatial resolution (HSR) imagery scene classification, it is a challenging task to recognize the high-level semantics from a large volume of complex HSR images. The probabilistic topic model (PTM), which focuses on modeling topics, has been proposed to bridge the so-called semantic gap. Conventional PTMs usually model the images with a dense semantic representation and, in general, one topic space is generated for all the different features. However, this approach fails to consider the sparsity of the semantic representation, the classification quality, as well as the time consumption. In this paper, to solve the above problems, a fully sparse semantic topic model (FSSTM) framework is proposed for HSR imagery scene classification. FSSTM, with an elaborately designed modeling procedure, is able to represent the image with sparse but representative semantics. Based on this framework, the topic weights of multiple features are exploited by solving a concave maximization problem, which improves the fusion of the discriminative semantic information at the topic level. Meanwhile, the sparsity and representativeness of the topics generated by FSSTM guarantee that the image is adaptive to the change of a topic number. FSSTM can consistently achieve a good performance with a limited number of training samples, and is robust for HSR image scene classification. The experimental results obtained with three different types of HSR image data sets confirm that the proposed algorithm is effective in improving the performance of scene classification, and is highly efficient in discovering the semantics of HSR images when compared with the state-of-the-art PTM methods.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Estimating Fractional Vegetation Cover From Landsat-7 ETM+ Reflectance
           Data Based on a Coupled Radiative Transfer and Crop Growth Model
    • Authors: Xiaoxia Wang;Kun Jia;Shunlin Liang;Qiangzi Li;Xiangqin Wei;Yunjun Yao;Xiaotong Zhang;Yixuan Tu;
      Pages: 5539 - 5546
      Abstract: Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE = 0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Two-Stage Focusing Algorithm for Highly Squinted Synthetic Aperture Radar
           Imaging
    • Authors: Lei Zhang;Guanyong Wang;Zhijun Qiao;Hongxian Wang;Ligang Sun;
      Pages: 5547 - 5562
      Abstract: Highly squinted synthetic aperture radar (SAR) data focusing is a challenging problem with difficulty to correct the severe range-azimuth coupling and motion errors. Squint minimization processing with the range-walk correction is widely adapted to simplify the decoupling processing, while it destructs the azimuth-shift invariance of conventional SAR transfer function. In this paper, a two-stage focusing algorithm (TSFA) is proposed to generate a focused imagery for the highly squinted airborne SAR. In the proposed algorithm, conventional range cell migration correction and azimuth matched filtering are performed and a fine focusing stage is established to correct the azimuth variance. In the fine focusing procedure, the coarse-focused image is divided into azimuth blocks to accommodate the correction of azimuth-variant residual range migration and phase terms. Moreover, precise motion compensation is embedded into the TSFA procedure to form an accurate airborne SAR imagery, which may be called the extended TSFA. In order to balance the processing precision and computational load, optimal selection of block size is investigated in detail. Both simulated and real measured airborne SAR data sets are used to validate the proposed approaches.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification
    • Authors: John McKay;Vishal Monga;Raghu G. Raj;
      Pages: 5563 - 5576
      Abstract: Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture sonar. Sophisticated classification techniques can now be used in sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC), which has shown an impressive resiliency to noise, blur, and occlusion. We present a coherent strategy for expanding upon SRC for sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric arrangements, bothersome Rayleigh noise, and unavoidable background clutter. Our method, pose-corrected sparsity (PCS), incorporates a novel interpretation of a spike and slab probability distribution toward use as a Bayesian prior for class-specific discrimination in combination with a dictionary learning scheme for localized patch extractions. Additionally, PCS offers the potential for anomaly detection in order to avoid false identifications of tested objects from outside the training set with no additional training required. Compelling results are shown using a database provided by the U.S. Naval Surface Warfare Center.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Efficient Ordinary Differential Equation-Based Discontinuous Galerkin
           Method for Viscoelastic Wave Modeling
    • Authors: Qiwei Zhan;Mingwei Zhuang;Qingtao Sun;Qiang Ren;Yi Ren;Yiqian Mao;Qing Huo Liu;
      Pages: 5577 - 5584
      Abstract: We present an efficient nonconformal-mesh discontinuous Galerkin (DG) method for elastic wave propagation in viscous media. To include the attenuation and dispersion due to the quality factor in time domain, several sets of auxiliary ordinary differential equations (AODEs) are added. Unlike the conventional auxiliary partial differential equation-based algorithm, this new method is highly parallel with its lossless counterpart, thus requiring much less time and storage consumption. Another superior property of the AODE-based DG method is that a novel exact Riemann solver can be derived, which allows heterogeneous viscoelastic coupling, in addition to accurate coupling with purely elastic media and fluid. Furthermore, thanks to the nonconformal-mesh technique, adaptive hp-refinement and flexible memory allocation for the auxiliary variables are achieved. Numerical results demonstrate the efficiency and accuracy of our method.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Deep Fully Convolutional Network-Based Spatial Distribution Prediction for
           Hyperspectral Image Classification
    • Authors: Licheng Jiao;Miaomiao Liang;Huan Chen;Shuyuan Yang;Hongying Liu;Xianghai Cao;
      Pages: 5585 - 5599
      Abstract: Most of the existing spatial-spectral-based hyperspectral image classification (HSIC) methods mainly extract the spatial-spectral information by combining the pixels in a small neighborhood or aggregating the statistical and morphological characteristics. However, those strategies can only generate shallow appearance features with limited representative ability for classes with high interclass similarity and spatial diversity and therefore reduce the classification accuracy. To this end, we present a novel HSIC framework, named deep multiscale spatial-spectral feature extraction algorithm, which focuses on learning effective discriminant features for HSIC. First, the well pretrained deep fully convolutional network based on VGG-verydeep-16 is introduced to excavate the potential deep multiscale spatial structural information in the proposed hyperspectral imaging framework. Then, the spectral feature and the deep multiscale spatial feature are fused by adopting the weighted fusion method. Finally, the fusion feature is put into a generic classifier to obtain the pixelwise classification. Compared with the existing spectral-spatial-based classification techniques, the proposed method provides the state-of-the-art performance and is much more effective, especially for images with high nonlinear distribution and spatial diversity.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters
    • Authors: Xudong Kang;Xiangping Zhang;Shutao Li;Kenli Li;Jun Li;Jón Atli Benediktsson;
      Pages: 5600 - 5611
      Abstract: A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Weighted Joint Collaborative Representation Based On Median-Mean Line and
           Angular Separation
    • Authors: Maryam Imani;Hassan Ghassemian;
      Pages: 5612 - 5624
      Abstract: Representation-based classifiers such as nearest regularized subspace (NRS) have been recently developed for hyperspectral image classification. The joint collaborative representation (JCR) and the weighted JCR (WJCR) methods added spatial information to the pixel-wise NRS classifier. While JCR adopts the same weights for extraction of spatial features from the surrounding pixels, WJCR uses the similarity between the central pixel and its surroundings to assign different weights to neighbor pixels. Two improved versions of WJCR are introduced in this paper. The first method, WJCR based on median-mean line, is proposed to cope with the negative effect of outlying neighbors. The second method, WJCR based on angular separation (AS), uses the benefits of the AS measurement to decrease the contribution of redundant information due to the highly correlated neighbors. The experimental results on some real hyperspectral data sets show the good efficiency of the proposed methods compared to other state-of-the-art NRS-based classifiers.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Coastal Sea-Level Measurements Based on GNSS-R Phase Altimetry: A Case
           Study at the Onsala Space Observatory, Sweden
    • Authors: Wei Liu;Jamila Beckheinrich;Maximilian Semmling;Markus Ramatschi;Sibylle Vey;Jens Wickert;Thomas Hobiger;Rüdiger Haas;
      Pages: 5625 - 5636
      Abstract: The characterization of global mean sea level is important to predict floods and to quantify water resources for human use and irrigation, especially in coastal regions. Recently, the application of global navigation satellite system reflectometry (GNSS-R) for water level monitoring has been successfully demonstrated. This paper focuses on the retrieval of sea surface height within a field experiment that was conducted at the Onsala Space Observatory (OSO) using the phase-based altimetry method. A continuous phase tracking algorithm, which relies on the GNSS amplitude and phase observations is proposed and works even under rough sea conditions at OSO's coast. Factors impacting the phase-based altimetry model, i.e., atmospheric propagation effects of the GNSS signals and influence of the GNSS-R observation instrument, are discussed. The relationship between the yield of coherent GNSS-R compared to the overall recorded events and the wind speed is investigated in detail. Ground-based sea-level measurements from June 10 to July 3, 2015 demonstrate that altimetric information about the reflecting water surface can be obtained with a root mean square error of 4.37 cm with respect to a reference tide gauge (TG) data set. The sea surface changes, derived from our field experiment and the reference TG, are highly correlated with a correlation coefficient of 0.93. The altimetric information can be retrieved even when the sea surface is very rough, corresponding to wind speeds up to 13 m/s. Moreover, the use of inexpensive conventional GNSS antennas shows that the system is useful for future large-scale sea level monitoring applications including numerous low-cost coastal ground stations.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Sequential Estimator: Toward Efficient InSAR Time Series Analysis
    • Authors: Homa Ansari;Francesco De Zan;Richard Bamler;
      Pages: 5637 - 5652
      Abstract: Wide-swath synthetic aperture radar (SAR) missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric SAR (InSAR) time series. However, the processing of the emerging Big Data is challenging for state-of-the-art InSAR analysis techniques. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by the compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. Such interferograms are used to link the “older” data batches with the most recent acquisitions and thus to reconstruct the phase time series. This scheme avoids the necessity of reprocessing the entire data stack at the face of each new acquisition. The proposed estimator introduces negligible degradation compared to the Cramer-Rao lower bound under realistic coherence scenarios. The estimator may therefore be adapted for high-precision near-real-time processing of InSAR and accommodate the conversion of InSAR from an offline to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to state-of-the-art techniques via simulations and application to Sentinel-1 data.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Integrating Multilayer Features of Convolutional Neural Networks for
           Remote Sensing Scene Classification
    • Authors: Erzhu Li;Junshi Xia;Peijun Du;Cong Lin;Alim Samat;
      Pages: 5653 - 5665
      Abstract: Scene classification from remote sensing images provides new possibilities for potential application of high spatial resolution imagery. How to efficiently implement scene recognition from high spatial resolution imagery remains a significant challenge in the remote sensing domain. Recently, convolutional neural networks (CNN) have attracted tremendous attention because of their excellent performance in different fields. However, most works focus on fully training a new deep CNN model for the target problems without considering the limited data and time-consuming issues. To alleviate the aforementioned drawbacks, some works have attempted to use the pretrained CNN models as feature extractors to build a feature representation of scene images for classification and achieved successful applications including remote sensing scene classification. However, existing works pay little attention to exploring the benefits of multilayer features for improving the scene classification in different aspects. As a matter of fact, the information hidden in different layers has great potential for improving feature discrimination capacity. Therefore, this paper presents a fusion strategy for integrating multilayer features of a pretrained CNN model for scene classification. Specifically, the pretrained CNN model is used as a feature extractor to extract deep features of different convolutional and fully connected layers; then, a multiscale improved Fisher kernel coding method is proposed to build a mid-level feature representation of convolutional deep features. Finally, the mid-level features extracted from convolutional layers and the features of fully connected layers are fused by a principal component analysis/spectral regression kernel discriminant analysis method for classification. For validation and comparison purposes, the proposed approach is evaluated via experiments with two challenging high-resolution remote sensing data sets, and shows the competitive perf-rmance compared with fully trained CNN models, fine-tuning CNN models, and other related works.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Bayesian Hyperspectral and Multispectral Image Fusions via Double Matrix
           Factorization
    • Authors: Baihong Lin;Xiaoming Tao;Mai Xu;Linhao Dong;Jianhua Lu;
      Pages: 5666 - 5678
      Abstract: This paper focuses on fusing hyperspectral and multispectral images with an unknown arbitrary point spread function (PSF). Instead of obtaining the fused image based on the estimation of the PSF, a novel model is proposed without intervention of the PSF under Bayesian framework, in which the fused image is decomposed into double subspace-constrained matrix-factorization-based components and residuals. On the basis of the model, the fusion problem is cast as a minimum mean square error estimator of three factor matrices. Then, to approximate the posterior distribution of the unknowns efficiently, an estimation approach is developed based on variational Bayesian inference. Different from most previous works, the PSF is not required in the proposed model and is not pre-assumed to be spatially invariant. Hence, the proposed approach is not related to the estimation errors of the PSF and has potential computational benefits when extended to spatially variant imaging system. Moreover, model parameters in our approach are less dependent on the input data sets and most of them can be learned automatically without manual intervention. Exhaustive experiments on three data sets verify that our approach shows excellent performance and more robustness to the noise with acceptable computational complexity, compared with other state-of-the-art methods.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Ku-/Ka-Band Extrapolation of the Altimeter Cross Section and Assessment
           With Jason2/AltiKa Data
    • Authors: Charles-Antoine Guérin;Jean-Christophe Poisson;Fanny Piras;Laiba Amarouche;Jean-Claude Lalaurie;
      Pages: 5679 - 5686
      Abstract: A simple extrapolation technique is proposed for the intercalibration of the Ku- and Ka-band altimeter data based on a recent analytical scattering model referred to as “GO4.” This method is tested with AltiKa and Jason2-Ku altimeters using one year of reprocessed data with the improved retracking algorithm ICENEW. The variations of the normalized radar cross section with respect to the main oceanic parameters are investigated in the Ku and Ka bands; the latter band is shown to have an increased sensitivity to wind speed, significant wave height as well as sea surface temperature. As a by-product of this analysis, we derive an original expression for the swell impact on the mean square slope, which allows to correct the GO4 model for the contribution of long waves. We show that the Ku/Ka prediction agrees within 0.25 dB with the respective levels of AltiKa and Jason2-Ku cross sections at wind speed larger than 4 m/s.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Bidirectional Polarized Reflectance Factors of Vegetation Covers:
           Influence on the BRF Models Results
    • Authors: Zhongqiu Sun;Di Wu;Yunfeng Lv;Yunsheng Zhao;
      Pages: 5687 - 5701
      Abstract: In this paper, we performed multiangular measurements spanning a wide viewing range in a hemisphere space for three types of vegetation cover and analyzed the bidirectional reflectance factor (BRF) measurements based on basic physical reflectance mechanisms to ensure the accuracy of the data. The measurements and the results with the best fitted model parameters were evaluated to determine whether the BRF models produce vegetation cover reflectance factor values that are qualitatively the same as the measured values. These models effectively characterized the BRF of the vegetation cover at most of the selected wavelengths (565, 670, and 865 nm). However, for planophile vegetation cover with smooth leaves, the current BRF models did not produce accurate values in the selected visible wavelength range; the average relative difference was approximately 0.3 at 670 nm. Subsequently, we subtracted the specular reflectance factor (calculated using the bidirectional polarized reflectance factors) from the total BRF and compared these data with the modeled results. The difference between the measured and modeled BRFs was notably decreased when we separated the specular reflectance factor at 670 nm for the planophile vegetation cover with smooth leaves. Moreover, there was a different degree of improvement in the agreement between the measured and modeled results, which depended on the wavelength and the type of vegetation cover. These results indicated that the subtraction of the specular reflectance factor effectively improved the capability of the BRF models to calculate the diffuse portion of the BRF of the vegetation cover.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Evaluating Scattering Contributions to C-Band Radar Backscatter From
           Snow-Covered First-Year Sea Ice at the Winter–Spring Transition Through
           Measurement and Modeling
    • Authors: Alexander S. Komarov;Jack C. Landy;Sergey A. Komarov;David G. Barber;
      Pages: 5702 - 5718
      Abstract: In this paper, we present model and measurement results for time-series angular dependencies of C-band nn and VV normalized radar cross-sections (NRCS) over first-year snow-covered sea ice during a winter-spring transition period. Experimental scatterometer and physical data were collected near Cambridge Bay, Nunavut, Canada, between May 20 and May 28, 2014, covering a severe storm event on May 25. We use the small perturbation scattering theory to model small-scale surface scattering, the Mie scattering theory to estimate the level of volume scattering in snow, and the Kirchhoff physical optics model to compute the large-scale surface scattering component. We observed good agreement between the model and experimental nn and VV NRCS. Before the storm, R2 between model and experimental NRCS was 0.88 and 0.82 for VV and nn, respectively. After the storm, R2 was 0.81 and 0.78 for VV and nn, respectively. Our model results suggest an overall increase in surface roughness after the storm event, supported by LiDAR measurements of the snow surface topography. Before the storm, the large-scale and small-scale surface scattering from the air-snow interface as well as volume scattering components dominated. After the storm, the large- and small-scale scattering contributions increased, while the volume scattering component considerably dropped. We attribute these effects to the increase in surface roughness and snow moisture content during the poststorm period. Our results could aid in interpretation of timeseries synthetic aperture radar images with respect to physical properties of snow and ice during the winter-spring transition period.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Analysis of Distribution Using Graphical Goodness of Fit for Airborne SAR
           Sea-Clutter Data
    • Authors: Zhihui Xin;Guisheng Liao;Zhiwei Yang;Yuhong Zhang;Hongxing Dang;
      Pages: 5719 - 5728
      Abstract: For radar target detection, the clutter distribution model needs to be identified first. The goodness of fit (GoF) between the original data and the assumed distribution can be used to choose the proper distribution model. Generally, the GoF is obtained using data histogram and theoretical distribution curve, and then the distribution model is judged via GoF. However, when the sample number is small, the histogram is rough and fluctuating, affecting the analysis of GoF. For the small sample, the graphical characteristic is obtained with the sample data to choose the most fitting distribution to the data in this paper. The graphical characteristic is acquired by a simpler process, that is, the original data are directly set as the test statistics, avoiding computing and sorting of other statistics. In this paper, the real airborne circular synthetic aperture radar data under different scan angles are analyzed using the GoF corresponding to histogram and graphical GoF, respectively. The results show that when the sea-clutter data histogram is close to two distributions, a more fitting distribution model may not be obtained by traditional GoF, but can be acquired by graphical representation. In addition, the sea data with different sight angles have different match properties. It is seen that the sea data are closer to the Rayleigh distribution in side-looking mode than that in big squint-angle mode, while the Weibull distribution and K distribution show equal fitting performance to sea clutter under variant radar sight angles.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • DeepCloud: Ground-Based Cloud Image Categorization Using Deep
           Convolutional Features
    • Authors: Liang Ye;Zhiguo Cao;Yang Xiao;
      Pages: 5729 - 5740
      Abstract: Accurate ground-based cloud image categorization is a critical but challenging task that has not been well addressed. One of the essential issues that affect the performance is to extract the representative visual features. Nearly all of the existing methods rely on the hand-crafted descriptors (e.g., local binary patterns, CENsus TRsansform hISTogram, and scale-invariant feature transform). Their limited discriminative power indeed leads to the unsatisfactory performance. To alleviate this, we propose “DeepCloud” as a novel cloud image feature extraction approach by resorting to the deep convolutional visual features. In the recent years, the deep convolutional neural network (CNN) has achieved the promising results in lots of computer vision and image understanding fields. Nevertheless, it has not been applied to cloud image classification yet. Thus, we actually pay the first effort to fill this blank. Since cloud image classification can be attributed to a multi-instance learning problem, simply employing the convolutional features within CNN cannot achieve the promising result. To address this, Fisher vector encoding is applied to executing the spatial feature aggregation and high-dimensional feature mapping on the raw deep convolutional features. Moreover, the hierarchical convolutional layers are used simultaneously to capture the fine textural characteristics and high-level semantic information in the unified manner. To further leverage the performance, a cloud pattern mining and selection method are also proposed. It targets at finding the discriminative local patterns to better distinguish the different kinds of clouds. The experiments on a challenging ground-based cloud image data set demonstrate the superiority of the proposition over the state-of-the-art methods.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • An Efficient Polyphase Filter-Based Resampling Method for Unifying the
           PRFs in SAR Data
    • Authors: Yoangel Torres;Kamal Premaratne;Falk Amelung;Shimon Wdowinski;
      Pages: 5741 - 5754
      Abstract: Variable higher pulse repetition frequencies (PRFs) are increasingly being used to meet the stricter requirements and complexities of current airborne and spaceborne synthetic aperture radar (SAR) systems associated with higher resolution and wider area products. POLYPHASE, the proposed resampling scheme, downsamples and unifies variable PRFs within a single look complex SAR acquisition and across a repeat pass sequence of acquisitions down to an effective lower PRF. A sparsity condition of the received SAR data ensures that the uniformly resampled data approximate the spectral properties of a decimated densely sampled version of the received SAR data. While experiments conducted with both synthetically generated and real airborne SAR data show that POLYPHASE retains comparable performance with the state-of-the-art best linear unbiased interpolation scheme in image quality, a polyphase filter-based implementation of POLYPHASE offers significant computational savings for arbitrary (not necessarily periodic) input PRF variations, thus allowing fully on-board, in-place, and real-time implementation.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Automated Detection of Ice and Open Water From Dual-Polarization
           RADARSAT-2 Images for Data Assimilation
    • Authors: Alexander S. Komarov;Mark Buehner;
      Pages: 5755 - 5769
      Abstract: In this paper, we present a new technique for automated detection of ice and open water from RADARSAT2 ScanSAR dual-polarization HH-HV images. Probability of the presence of ice within 2.05 km × 2.05 km areas is modeled using a form of logistic regression as a function of the difference between the wind speeds estimated from synthetic aperture radar (SAR) data and those obtained from numerical weather prediction short-term forecasts, the spatial correlation between HH and HV backscatter signals, and the spatial standard deviation of the wind speed estimated from SAR. The resulting ice probability model was built based on thousands of SAR images and corresponding Canadian Ice Service (CIS) Image Analysis products covering all seasons and all Canadian and adjacent Arctic regions being monitored by CIS. Extensive verification of the proposed technique was conducted for an entire year (2013) against independent Image Analysis products and Interactive Multisensor Snow and Ice Mapping System ice extent products. Using a probability threshold of 0.95, 72.2% of the retrievals were classified as either ice or open water with an accuracy of 99.2% in the most clean verification scenario against Image Analysis pure ice and water data. The ability to obtain such a large number of retrievals with a very high accuracy makes it feasible to assimilate the resulting retrievals in an ice prediction system. Consequently, the developed ice/water retrieval technique will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Recent Improvements to Suomi NPP Ozone Mapper Profiler Suite Nadir Mapper
           Sensor Data Records
    • Authors: Chunhui Pan;Fuzhong Weng;T. Beck;L. Flynn;Shouguo Ding;
      Pages: 5770 - 5776
      Abstract: The Ozone Mapping and Profiler Suite (OMPS) is carried onboard on the Suomi National Polar-orbiting Partnership satellite which was launched on October 28, 2011. Since its launch, many changes in radiometric and spectrometric calibration have been made to improve the OMPS sensor data quality. The most challenging issue is to correct an unexpected variation of the in-flight wavelength scale in the OMPS Nadir Mapper (NM) spectrometer. Validation of the NM earth viewing albedo estimates of 2% to 5% wavelength-dependent errors across the sensor spatial instantaneous field of views (IFOVs). The root cause attributes these errors to a large variation in the wavelength registration for each of the NM charge-coupled device earth view pixels. Recent calibration change has significantly improved the total wavelength knowledge accuracy, and as a result the NM wavelength-dependent albedo uncertainty is reduced below the requirement of 2% for most of the channels across all of the spatial IFOVs.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Copula-Based Joint Statistical Model for Polarimetric Features and Its
           Application in PolSAR Image Classification
    • Authors: Hao Dong;Xin Xu;Haigang Sui;Feng Xu;Junyi Liu;
      Pages: 5777 - 5789
      Abstract: Polarimetric features are essential to polarimetric synthetic aperture radar (PolSAR) image classification for their better physical understanding of terrain targets. The designed classifiers often achieve better performance via feature combination. However, the simply combination of polarimetric features cannot fully represent the information in PolSAR data, and the statistics of polarimetric features are not extensively studied. In this paper, we propose a joint statistical model for polarimetric features derived from the covariance matrix. The model is based on copula for multivariate distribution modeling and alpha-stable distribution for marginal probability density function estimations. We denote such model by CoAS. The proposed model has several advantages. First, the model is designed for real-valued polarimetric features, which avoids the complex matrix operations associated with the covariance and coherency matrices. Second, these features consist of amplitudes, correlation magnitudes, and phase differences between polarization channels. They efficiently encode information in PolSAR data, which lends itself to interpretability of results in the PolSAR context. Third, the CoAS model takes advantage of both copula and the alpha-stable distribution, which makes it general and flexible to construct the joint statistical model accounting for dependence between features. Finally, a supervised Markovian classification scheme based on the proposed CoAS model is presented. The classification results on several PolSAR data sets validate the efficacy of CoAS in PolSAR image modeling and classification. The proposed CoAS-based classifiers yield superior performance, especially in building areas. The overall accuracies are higher by 5%-10%, compared with other benchmark statistical model-based classification techniques.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Wavelet-Based Optical Flow Estimation of Instant Surface Currents From
           Shore-Based and UAV Videos
    • Authors: Pierre Dérian;Rafael Almar;
      Pages: 5790 - 5797
      Abstract: Instant fields of surface current are retrieved from shore-based and unmanned aerial vehicle videos by an optical flow (OF) method named “Typhoon.” This computer vision algorithm estimates dense 2-D 2-component velocity fields from the observable motion of foam patterns in the surf zone. Despite challenging image data resolution and quality, comparison of OF surface current estimates with measurements by an acoustic Doppler velocimeter reveals its ability to capture both wave-to-wave fluctuations and low-frequency variations. The method is also successfully applied to the monitoring of a “flash rip” event. This paper shows clearly the high potential of this method in the nearshore, where the rapid development of webcams and drones offers a large number of applications for swimming and surfing safety, engineering and naval security, and research purpose, by providing quantitative information.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Two-Stage Reranking for Remote Sensing Image Retrieval
    • Authors: Xu Tang;Licheng Jiao;William J. Emery;Fang Liu;Dan Zhang;
      Pages: 5798 - 5817
      Abstract: Image reranking is a popular postprocessing method for remote sensing image retrieval (RSIR), which aims at enhancing the initial retrieval performance. In general, it takes either users' opinions or the relationships between images into consideration to find an optimal reranked list based on the initial retrieved results. In this paper, we present a reranking method for improving RSIR, which is named two-stage reranking (TSR). Suppose the k-nearest neighbors of a query RS image have been obtained by the initial retrieval. The first step of our TSR is to edit these neighbors using the editing scheme. A handful of informative and representative RS images are selected by the active learning algorithm, and their binary labels are provided by the users relative to the query image. Then, a binary classifier is trained using the selected RS images and their labels to classify the rest of the neighbors. Finally, both classification results and rank information in the initial retrieval results are considered to decide which neighbor should be excluded. In the next step, the remaining RS images are reranked by the proposed reranking scheme, i.e., multisimilarity fusion reranking. Both the user's experience and image relationships are taken into account in TSR to ensure the performance of the reranking. The efficiency and the robustness of our method are validated by experiments conducted on two different types of RS images. Compared with the existing visual reranking approaches, our method achieves improved performance.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • An End-Member-Based Two-Source Approach for Estimating Land Surface
           Evapotranspiration From Remote Sensing Data
    • Authors: Ronglin Tang;Zhao-Liang Li;
      Pages: 5818 - 5832
      Abstract: Evapotranspiration (ET) is one of the key variables in the water and energy exchange between land surface and atmosphere. This paper develops an end-member-based two-source approach for estimating land surface ET (i.e., the ESVEP model) from remote sensing data, considering the differing responses of soil water content at the upper surface layer to soil evaporation and at the deeper root zone layer to vegetation transpiration. The ESVEP model first diverges the soil-vegetation system net radiation into soil and vegetation components by considering the transmission of direct and diffuse shortwave radiation separately from the transmission of longwave radiation through the canopy, then calculates the four dry/wet soil/vegetation end-members with the diverged soil and vegetation net radiations, and last separates soil evaporation from vegetation transpiration based on the two-phase ET dynamics and the four end-member temperatures. The model can overall produce reasonably good surface energy fluxes and is no more sensitive to meteorology, vegetation, and remote sensing inputs than other two-source energy balance models and surface temperature versus vegetation index ($T_{R}$ -VI) trapezoid models. A reasonable agreement could be found with a small bias of ±8 W/$\text{m}^{2}$ and a root-mean-square error within 60 W/$\text{m}^{2}$ (comparable to accuracies published in other studies) when both model-estimated sensible heat flux and latent heat flux from MODIS remote sensing data are validated with ground-based large aperture scintillometer measurements.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Radar Sounding Through the Earth’s Ionosphere at 45 MHz
    • Authors: Anthony Freeman;Xiaoqing Pi;Essam Heggy;
      Pages: 5833 - 5842
      Abstract: Radar sounding from aircraft or ground-coupled radars has long provided scientists with a powerful technique to sound through ice layers to retrieve local depth and layering structure. More recently, it has been used to detect shallow aquifers in warm, dry, and desert regions. At Mars, a long-wavelength radar sounding from low orbit altitudes has produced global maps that reveal the presence of ice layering at all latitudes and glacial deposits on the flanks of volcanoes. Until now, sounding from the earth orbit at wavelengths long enough to penetrate ice sheets and arid sand was thought to be infeasible, because of the electromagnetic properties of the ionosphere. In this paper, we show that a radar sounding at frequencies as low as 45 MHz is, in fact, theoretically possible under viewing conditions that occur often enough to be practical. This conclusion opens up a previously unutilized portion of the electromagnetic spectrum for large-scale, spaceborne remote sensing of subsurface features on the earth.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Onto a Skewness Approach to the Generalized Curvature Ocean Surface
           Scattering Model
    • Authors: Faozi Saïd;Harald Johnsen;Frédéric Nouguier;Bertrand Chapron;Geir Engen;
      Pages: 5843 - 5853
      Abstract: The generalized curvature ocean surface scattering model [general curvature model (GCM)] is extended and revisited. Two key steps are addressed in this paper, namely, a necessary sea surface spectrum undressing procedure and the inclusion of a skewness phase-related component. Normalized radar cross-section (NRCS) simulations are generated at C-band for various wind conditions, polarizations, and incidence angles. Results are compared with CMOD5.n. Although the sea surface spectrum undressing procedure is a necessary step, the overall NRCS dynamic is notably affected only in low wind conditions (≤5 m/s). The inclusion of the skewness phase-related component makes the most impact to the NRCS dynamic where the upwind/downwind asymmetry is clearly detectable. A good agreement between the upwind/downwind asymmetry of the extended GCM and CMOD5.n is achieved for moderate winds (≈5-10 m/s) and moderate incidence angles (≈32°-40°). For low incidence angles (
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Extended Kalman Filter for Multichannel InSAR Height Reconstruction
    • Authors: Roberto Ambrosino;Fabio Baselice;Giampaolo Ferraioli;Gilda Schirinzi;
      Pages: 5854 - 5863
      Abstract: One of the main challenges in Interferometric Synthetic Aperture Radar (SAR) is the accurate height reconstruction of the observed scene. Recently, approaches based on Extended Kalman Filter (EKF) have been proposed. Most of them are based on the hypothesis of height profile continuity. Such condition greatly reduces their applicability, being only valid for particular scenarios. Within this paper, we present a novel Kalman-based height reconstruction approach, specifically designed to work with multichannel data related to any type of scenario, both smooth or sharp. The novelty of the technique consists in its ability in detecting and correctly handling sharp height discontinuities while regularizing smooth areas. The approach is able to maintain the high computational efficiency typical of EKF and to work in an almost unsupervised way. The methodology has been tested and validated on both simulated and real X-band (TerraSAR-X and COSMO-SkyMed) high-resolution data sets. Reported results are encouraging and interesting, showing the correctness and the validity of the proposed approach.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • An Information Theory-Based Scheme for Efficient Classification of Remote
           Sensing Data
    • Authors: Andrea Marinoni;Gianni Cristian Iannelli;Paolo Gamba;
      Pages: 5864 - 5876
      Abstract: Information theory has recently become an interesting topic in earth observation data management and analysis, since it can provide important information on hidden interactions and correlations among the considered data records. Although several methods have been proposed and implemented to efficiently extract a proper set of features and deliver accurate image investigation, classification, and segmentation, these architectures show drawbacks when the data sets are characterized by complex interactions among the samples. In this paper, a new approach based on information theory for automatic pattern recognition is introduced for accurate classification of remotely sensed data. Experimental results carried out on real data sets show the validity of the proposed approach.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Land Surface Temperature Estimate From Chinese Gaofen-5 Satellite Data
           Using Split-Window Algorithm
    • Authors: Xin Ye;Huazhong Ren;Rongyuan Liu;Qiming Qin;Yao Liu;Jijia Dong;
      Pages: 5877 - 5888
      Abstract: The Gaofen-5 (GF-5) satellite, the only satellite that provides the thermal infrared (TIR) sensor in the national high-resolution earth observation project of China, will observe earth surface at a spatial resolution of 40 m in four TIR channels. This paper aims at developing a new nonlinear, four-channel split-window (SW) algorithm to retrieve land surface temperature (LST) from GF-5 image. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapors (CWV) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the new algorithm can obtain LST with root-mean-square errors of less than 1 K. Compared with previous two- and three-channel SW algorithms, the four-channel SW algorithm obtained better results in estimating LST, especially under moist atmospheres. Methods of estimating CWV and pixel emissivity were also conducted. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapor demonstrated the good performance of the proposed algorithm. At last, the new SW algorithm was validated using ground-measured data at six sites, and some simulated images from airborne hyperspectral TIR data.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Improving the Algorithm of Extracting Regional Total Precipitable Water
           Vapor Over Land From MODIS Images
    • Authors: Mohammad Hossein Merrikhpour;Majid Rahimzadegan;
      Pages: 5889 - 5898
      Abstract: Precise estimation of total precipitable water vapor (TPW) with high temporal and spatial resolutions is of great importance in different disciplines. Moderate-Resolution Imaging Spectroradiometer (MODIS) is one of the sensors which have absorption and nonabsorption bands of water vapor. There is a standard algorithm to produce TPW product of MODIS (MOD05/MYD05) which uses the ratios of reflectances in strong, moderate, and weak absorption bands of water vapor to nonabsorption ones (transmission). This paper aims to present a method based on this algorithm to optimize TPW estimation in local scale. To do so, the western part of Iran was chosen as the study region. Terra MODIS images and MOD05 in clear-sky conditions related to the 100 days in four seasons of 2015-2016 were provided as the selected data. To validate and improve the results, TPW measured in six radiosonde stations and interpolated for overpass time of Terra was utilized. Four procedures were performed. In the first procedure, the coefficients of transmissions were extracted using linear least-squares technique, separately. For the second procedure, the coefficients were calculated in terms of the highest atmospheric transmission sensitivity to TPW for each absorption band separately, and in the third procedure, they were calculated simultaneously. In the last procedure, the errors from third one were modeled with a linear relationship between reflectance ratios of absorption bands. Based on the results, in highest accuracy, the coefficient of determination R2 and Root Mean Square Error was 0.878 and 2.702 mm, respectively, which were acceptable comparing those of other researchers.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Command and Control for Multifunction Phased Array Radar
    • Authors: Mark E. Weber;John Y. N. Cho;Henry G. Thomas;
      Pages: 5899 - 5912
      Abstract: We discuss the challenge of managing the Multifunction Phased Array Radar (MPAR) timeline to satisfy the requirements of its multiple missions, with a particular focus on weather surveillance. This command and control (C2) function partitions the available scan time among these missions, exploits opportunities to service multiple missions simultaneously, and utilizes techniques for increasing scan rate where feasible. After reviewing the candidate MPAR architectures and relevant previous research, we describe a specific C2 framework that is consistent with a demonstrated active array architecture using overlapped subarrays to realize multiple, concurrent receive beams. Analysis of recently articulated requirements for near-airport and national-scale aircraft surveillance indicates that with this architecture, 40-60% of the MPAR scan timeline would be available for the high-fidelity weather observations currently provided by the Weather Service Radar (WSR-88D) network. We show that an appropriate use of subarray generated concurrent receive beams, in concert with previously documented, complementary techniques to increase the weather scan rate, could enable MPAR to perform full weather volume scans at a rate of 1 per minute. Published observing system simulation experiments, human-in-the-loop studies and radar-data assimilation experiments indicate that high-quality weather radar observations at this rate may significantly improve the lead time and reliability of severe weather warnings relative to current observation capabilities.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Detection of Cars in High-Resolution Aerial Images of Complex Urban
           Environments
    • Authors: Mohamed ElMikaty;Tania Stathaki;
      Pages: 5913 - 5924
      Abstract: Detection of small targets, more specifically cars, in aerial images of urban scenes, has various applications in several domains, such as surveillance, military, remote sensing, and others. This is a tremendously challenging problem, mainly because of the significant interclass similarity among objects in urban environments, e.g., cars and certain types of nontarget objects, such as buildings' roofs and windows. These nontarget objects often possess very similar visual appearance to that of cars making it hard to separate the car and the noncar classes. Accordingly, most past works experienced low precision rates at high recall rates. In this paper, a novel framework is introduced that achieves a higher precision rate at a given recall than the state of the art. The proposed framework adopts a sliding-window approach and it consists of four stages, namely, window evaluation, extraction and encoding of features, classification, and postprocessing. This paper introduces a new way to derive descriptors that encode the local distributions of gradients, colors, and texture. Image descriptors characterize the aforementioned cues using adaptive cell distributions, wherein the distribution of cells within a detection window is a function of its dominant orientation, and hence, neither the rotation of the patch under examination nor the computation of descriptors at different orientations is required. The performance of the proposed framework has been evaluated on the challenging Vaihingen and Overhead Imagery Research data sets. Results demonstrate the superiority of the proposed framework to the state of the art.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Influence of Surface Roughness Measurement Scale on Radar Backscattering
           in Different Agricultural Soils
    • Authors: Alex Martinez-Agirre;Jesús Álvarez-Mozos;Hans Lievens;Niko E. C. Verhoest;
      Pages: 5925 - 5936
      Abstract: Soil surface roughness strongly affects the scattering of microwaves on the soil surface and determines the backscattering coefficient (σ0) observed by radar sensors. Previous studies have shown important scale issues that compromise the measurement and parameterization of roughness especially in agricultural soils. The objective of this paper was to determine the roughness scales involved in the backscattering process over agricultural soils. With this aim, a database of 132 5-m profiles taken on agricultural soils with different tillage conditions was used. These measurements were acquired coinciding with a series of ENVISAT/ASAR observations. Roughness profiles were processed considering three different scaling issues: 1) influence of measurement range; 2) influence of low-frequency roughness components; and 3) influence of high-frequency roughness components. For each of these issues, eight different roughness parameters were computed and the following aspects were evaluated: 1) roughness parameters values; 2) correlation with σ0; and 3) goodness-of-fit of the Oh model. Most parameters had a significant correlation with σ0 especially the fractal dimension, the peak frequency, and the initial slope of the autocorrelation function. These parameters had higher correlations than classical parameters such as the standard deviation of surface heights or the correlation length. Very small differences were observed when longer than 1-m profiles were used as well as when small-scale roughness components (100 cm) were disregarded. In conclusion, the medium-frequency roughness components (scale of 5-100 cm) seem to be the most influential scales in the radar backscattering process on agricultural soils.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Measurement of the Ionospheric Scintillation Parameter $C_{k}L$ From SAR
           Images of Clutter
    • Authors: David P. Belcher;Christopher R. Mannix;Paul S. Cannon;
      Pages: 5937 - 5943
      Abstract: Space-based synthetic aperture radar (SAR) can be affected by the ionosphere, particularly at L-band and below. A technique is described that exploits the reduction in SAR image contrast to measure the strength of ionospheric turbulence parameter CkL. The theory describing the effect of the ionosphere on the SAR point spread function (PSF) and the consequent effect on clutter is reviewed and extended. This theory can then be used to determine CkL from both corner reflectors (CRs) and K-distributed SAR clutter. Measuring the K-distribution order parameter allows CkL values much lower than those that defocus the image to be determined. The results of an experiment in which a CR on Ascension Island was repeatedly imaged by PALSAR-2 in the spotlight mode during the scintillation season are described. The value of CkL obtained by measuring the clutter was compared with that obtained from a nearby CR. The correlation between the two was good using a median value of the spectral index p. This correlation was improved by using the measured value of p derived from the CR PSF. The technique works for any homogeneous K-distributed SAR clutter and is thus applicable to extra-terrestrial bodies as well as PALSAR-2 images of Ascension Island.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Lunar Brightness Temperature Model Based on the Microwave Radiometer Data
           of Chang’e-2
    • Authors: Zhanchuan Cai;Ting Lan;
      Pages: 5944 - 5955
      Abstract: The brightness temperature (TB) data of the Moon acquired by the microwave radiometer (MRM) on-board the Chinese Chang'e-2 (CE-2) lunar probe are valuable and comprehensive data, which can be helpful in studying the physical properties of the lunar regolith, such as thickness, physical temperature, and dielectric constant. To construct the accurate and high-resolution lunar TB model with the TB data obtained by the MRM on-board CE-2, 2401 tracks of the original TB data are quantized by using the hour angle processing, and the hierarchical MK splines function (HMKSF) method is presented, which uses a hierarchy of coarse-to-fine control lattices to generate a sequence of TB model functions. The TB model constructor is the sum of the TB model functions derived at each level of the hierarchy. In addition, the lunar TB models with a resolution of 0.5°×0.5° in all four frequency channels are constructed for both the daytime and the nighttime. The obtained models show rich information, e.g., the global distribution of TB over the lunar surface, the effect of frequency on the TB model.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Improvement of Lake Ice Thickness Retrieval From MODIS Satellite Data
           Using a Thermodynamic Model
    • Authors: Homa Kheyrollah Pour;Claude R. Duguay;K. Andrea Scott;Kyung-Kuk Kang;
      Pages: 5956 - 5965
      Abstract: Observations of ice thickness are limited in high latitude regions, at a time when they are increasingly being requested by operational ice centers. This study aims to improve the retrieval of lake ice thickness using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Aqua (P.M.) and Terra (A.M.) satellites. The accuracy of ice thickness retrievals based on MODIS lake ice surface temperature (LIST) is investigated using a commonly used heat balance equation and the retrieved ice thicknesses are compared to in situ measurements from the Canadian Ice Service. The accuracy of ice thickness estimates is improved when using snow depth from the 1-D thermodynamic lake ice model Canadian Lake Ice Model (CLIMo) rather than an empirical relationship between snow depth and ice thickness utilized in the recent investigations. Taking into account all data over the study period (2002-2014), the mean bias error and the root-mean-square error are reduced from -0.42 to 0.07 m and 0.58 to 0.17 m, respectively, with the novel approach proposed herein. However, this approach is limited to ice thickness estimations of less than ca. 1.7 m.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Spectral-Density-Based Graph Construction Techniques for Hyperspectral
           Image Analysis
    • Authors: Jeffrey R. Stevens;Ronald G. Resmini;David W. Messinger;
      Pages: 5966 - 5983
      Abstract: The past decade has seen the emergence of many hyperspectral image (HSI) analysis algorithms based on graph theory and derived manifold coordinates. The performance of these algorithms is inextricably tied to the graphical model constructed from the spectral data, i.e., the community structure of the spectral data must be well represented to extract meaningful information. This paper provides a survey of many spectral graph construction techniques currently used by the hyperspectral community and discusses their advantages and disadvantages for hyperspectral analyses. A focus is provided on techniques influenced by spectral density from which the concept of community structure arises. Two inherently density-weighted graph construction techniques from the data mining literature, shared nearest neighbor (NN) and mutual proximity, are also introduced and compared as they have not been previously employed in HSI analyses. Density-based edge allocation is demonstrated to produce more uniform NN lists than nondensity-based techniques by demonstrating an increase in the number of intracluster edges and improved k-NN classification performance. Imposing the mutuality constraint to symmetrify an adjacency matrix is demonstrated to be beneficial in most circumstances, especially in rural (less cluttered) scenes. Surprisingly, many complex edgereweighting techniques are shown to slightly degrade NN list characteristics. An analysis suggests this condition is possibly attributable to the validity of characterizing spectral density by a single variable representing data scale. As such, these complex edge-reweighting techniques may need to be modified to increase their effectiveness, or simply not be used.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension
           of the Split Range-Spectrum Technique
    • Authors: Heresh Fattahi;Mark Simons;Piyush Agram;
      Pages: 5984 - 5996
      Abstract: Repeat pass interferometric synthetic aperture radar (InSAR) observations may be significantly impacted by the propagation delay of the microwave signal through the ionosphere, which is commonly referred to as ionospheric delay. The dispersive character of the ionosphere at microwave frequencies allows one to estimate the ionospheric delay from InSAR data through a split range-spectrum technique. Here, we extend the existing split range-spectrum technique to InSAR time-series. We present an algorithm for estimating a time-series of ionospheric phase delay that is useful for correcting InSAR time-series of ground surface displacement or for evaluating the spatial and temporal variations of the ionosphere's total electron content (TEC). Experimental results from stacks of L-band SAR data acquired by the ALOS-1 Japanese satellite show significant ionospheric phase delay equivalent to 2 m of the temporal variation of InSAR time-series along 445 km in Chile, a region at low latitudes where large TEC variations are common. The observed delay is significantly smaller, with a maximum of 10 cm over 160 km, in California. The estimation and correction of ionospheric delay reduces the temporal variation of the InSAR time-series to centimeter levels in Chile. The ionospheric delay correction of the InSAR time-series reveals earthquake-induced ground displacement, which otherwise could not be detected. A comparison with independent GPS time-series demonstrates an order of magnitude reduction in the root mean square difference between GPS and InSAR after correcting for ionospheric delay. The results show that the presented algorithm significantly improves the accuracy of InSAR time-series and should become a routine component of InSAR time-series analysis.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Hyperspectral and Multispectral Image Fusion Based on Local Low Rank and
           Coupled Spectral Unmixing
    • Authors: Yuan Zhou;Liyang Feng;Chunping Hou;Sun-Yuan Kung;
      Pages: 5997 - 6009
      Abstract: Hyperspectral images (HSIs) usually have high spectral and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolution. The fusion of HSI and MSI aims to create spectral images with high spectral and spatial resolution. In this paper, we propose a fusion algorithm by combining linear spectral unmixing with the local low-rank property. By taking advantage of the local low-rank property, we first partition the corresponding spectral image into patches. For each patch pair, we cast the fusion problem as a coupled spectral unmixing problem that extracts the abundance and the endmembers of MSI and HSI, respectively. It then updates the abundance and the endmember through an alternating update algorithm. In fact, the convergence of the alternative update algorithm can be mathematically and empirically supported. We also propose a multiscale postprocessing procedure to combine fusion results obtained under different patch sizes. In experiments on three data sets, the proposed fusion algorithms outperformed state-of-the-art fusion algorithms in both spatial and spectral domains.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Modeling the Temporal Variability of Thermal Emissions From Row-Planted
           Scenes Using a Radiosity and Energy Budget Method
    • Authors: Zunjian Bian;Yongming Du;Hua Li;Biao Cao;Huaguo Huang;Qing Xiao;Qinhuo Liu;
      Pages: 6010 - 6026
      Abstract: Land surface temperature (LST) is often needed for using remotely sensed data to study the surface energy budget and hydrological cycle. However, LST is challenging to measure and simulate because of its high sensitivity to atmospheric instability and solar angle, particularly over large-scale heterogeneous scenes. We propose a model that combines radiosity theory and an energy budget method for surface temperatures; we also explore the anisotropic behavior of row-planted crop emissions. The surface thermodynamic equilibrium state is fulfilled via the interaction between the 3-D radiative transfer calculations of the thermal-region radiosity-graphics combined model and the energy balance equation. Despite its shortcomings, such as the time-consuming calculations, the proposed model is feasible according to the results of an intercomparison and validation analysis. The intercomparison shows that the model exhibits similar performance, in terms of surface temperature calculations, to that of the soil-canopy observation, photochemistry and energy balance model (root-mean-square differences) of 0.59 °C and 1.77 °C for the leaf and soil components, respectively. Excellent agreement with the observed directional variation over summer maize canopies is also obtained, with R2 values exceeding 0.6 and a mean RMSE of 0.32 °C. Thus, we recommend the new combined model as an option for explaining directional anisotropy due to its potential application to 3-D scenes.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
  • Introducing IEEE Collabratec
    • Pages: 6027 - 6027
      Abstract: Advertisement, IEEE. IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical interests, or career pursuits. You can also create and share a professional identity that showcases key accomplishments and participate in groups focused around mutual interests, actively learning from and contributing to knowledgeable communities. All in one place! Learn about IEEE Collabratec at ieeecollabratec.org.
      PubDate: Oct. 2017
      Issue No: Vol. 55, No. 10 (2017)
       
 
 
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