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Geoscience and Remote Sensing, IEEE Transactions on
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 181  
  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: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • 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: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Presents the GRSS society institutional listings.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and
           Higher Order MRF
    • Authors: Huan Luo;Cheng Wang;Chenglu Wen;Ziyi Chen;Dawei Zai;Yongtao Yu;Jonathan Li;
      Pages: 3631 - 3644
      Abstract: Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • An Alternative Method for Estimating 3-D Large Displacements of Mining
           Areas from a Single SAR Amplitude Pair Using Offset Tracking
    • Authors: Zefa Yang;Zhiwei Li;Jianjun Zhu;Axel Preusse;Jun Hu;Guangcai Feng;Huiwei Yi;Markus Papst;
      Pages: 3645 - 3656
      Abstract: Measuring 3-D mining-induced displacements is essential to understand mining deformation mechanisms and assess mining-related geohazards. In our previous work, we proposed a method for estimating 3-D mining-induced large displacements with the surface deformation along the radar line-of-sight (LOS) direction derived from a single amplitude pair (SAP) of synthetic aperture radar (SAR) using the offset tracking (OT) procedure (hereafter referred to as OT-SAP). The OT-SAP method effectively reduces the strict requirements on SAR data of the previous OT-based methods for 3-D mining-induced displacement retrieval. However, OT-SAP is not robust to errors in the LOS deformation, due to the lack of redundant observations. In this paper, we present an alternative approach (hereafter called AOT-SAP) to OT-SAP. The AOT-SAP method involves estimating the 3-D mining-induced large displacements with OT-derived 2-D deformation observations along the LOS and azimuth directions from an SAP of SAR, instead of just the LOS deformation in the OT-SAP method. Consequently, more redundant observations are incorporated in the AOT-SAP method compared with the previous OT-SAP method. The theoretical analysis and experiments based on both simulated and real data sets suggest that AOT-SAP can effectively improve the accuracies of the estimated 3-D displacements compared with the OT-SAP-estimated ones.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • High-Resolution Ice-Sounding Radar Measurements of Ice Thickness Over East
           Antarctic Ice Sheet as a Part of Chinese National Antarctic Research
    • Authors: Xiaojun Liu;Shinan Lang;Bo Zhao;Feng Zhang;Qing Liu;Chuanjun Tang;Dezhi Li;Guangyou Fang;
      Pages: 3657 - 3666
      Abstract: This paper presents the ice thickness, fine resolution internal reflecting horizons (IRHs), and distinct bottom topography measurements of Chinese Kunlun Station and Grove Mountains, Antarctica, derived from sounding these glaciers with a high-resolution radar. To enable the development of next-generation ice-sheet models, we need information on IRHs, bottom topography, and basal conditions. To this end, we performed measurements with the progressively improved ice-sounding radar system, currently known as the high-resolution ice-sounding radar developed by the Key Laboratory of Electromagnetic Radiation and Sensing Technology of Institute of Electronics, Chinese Academy of Sciences, Beijing, China. We processed the collected data using focused synthetic aperture radar (SAR) algorithm named the modified range migration algorithm using curvelets and the modified nonlinear chirp scaling algorithm to improve radar sensitivity and reduce along-track surface clutter. Representative results from selected transects indicate that we successfully sounded 3-km-thick ice with a fine resolution of 0.75 m. In this paper, we provide a brief description of the radar system, discuss the focused SAR processing algorithms, and provide sample results to demonstrate the successful sounding of the ice sheet in Antarctica.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Saliency-Based Endmember Detection for Hyperspectral Imagery
    • Authors: Xinyu Wang;Yanfei Zhong;Yao Xu;Liangpei Zhang;Yanyan Xu;
      Pages: 3667 - 3680
      Abstract: This paper focuses on the endmember extraction (EE) technique for analyzing hyperspectral images. We first prove that the reconstruction errors (REs) and abundance anomalies (AAs) (abundances that fail to satisfy the abundance constraints) are effective in extracting undetected endmembers. Then, according to the spatial continuity of the endmember objects and differing from noise or outliers with a sparse distribution, the endmembers are assumed to be located at some salient areas in the RE and AA maps. A novel EE algorithm termed saliency-based endmember detection (SED) is proposed, where the visual saliency model is introduced to explore and analyze the spatial information that is contained in the AA and RE maps. Specifically, the AA and RE maps are regarded as the visual inputs, whereas the endmembers are treated as the visual stimuli. In SED, we assume that the pure pixel assumption holds. Based on the characteristics of the human visual system, the proposed method can not only extract endmembers in homogenous areas, but it can also highlight the small targets whose abundances may be spatially varied. In addition, since the spatial information is exploited in the reconstruction, the capability of the endmembers to represent the hyperspectral scene is automatically considered in the process of EE, and the detected endmembers are both accurate and reliable. The experimental results obtained on both simulated and real hyperspectral data confirm the merits and viability of the proposed algorithm.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Lunar Crater Detection Based on Terrain Analysis and Mathematical
           Morphology Methods Using Digital Elevation Models
    • Authors: Min Chen;Danyang Liu;Kejian Qian;Jun Li;Mengling Lei;Yi Zhou;
      Pages: 3681 - 3692
      Abstract: Lunar impact craters are the most typical geomorphic feature on the moon and are of great importance in studies of lunar terrain features. This paper presents a crater detection algorithm (CDA) that is based on terrain analysis and mathematical morphology methods. The proposed CDA is applied to digital elevation models (DEMs) to identify the boundaries of impact craters. The topographic and morphological characteristics of impact craters are discussed, and detailed steps are presented to detect different types of craters, such as dispersal craters, connective craters, and con-craters. The DEM from the Lunar Reconnaissance Orbiter, which has a resolution of 100 m, is used to verify the proposed CDA. The results show that the boundaries of impact craters can be detected. The results enable increased understanding of surface processes through the characterization of crater morphometry and the use of crater size-frequency distributions to estimate the ages of planetary surfaces.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • 3-D Interferometric Inverse Synthetic Aperture Radar Imaging of Ship
           Target With Complex Motion
    • Authors: Yong Wang;Xuefei Chen;
      Pages: 3693 - 3708
      Abstract: A novel algorithm for 3-D interferometric inverse synthetic aperture radar (InISAR) imaging of ship target with complex motion via orthogonal double baseline is presented. For the ship target with a certain translational velocity and 3-D rotation, the distance between any scatterers on the target and the radar is analyzed in detail, and the keystone transform is used to reduce the impact of migration through resolution cell of ship target with big size. Then, the fractional Fourier transform is adopted to achieve the 2-D ISAR image of the target, and the mismatch of the ISAR images achieved by the three radars is solved by the image coregistration method according to the 1-D range profile. Finally, the 3-D InISAR image of the ship target is achieved with the interferometric operation with the three ISAR images. The effectiveness of the proposed method is proved by some simulation results, and the influence of different motion parameters on the 3-D imaging of ship target is analyzed simultaneously in this paper.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • HF (5 MHz) Imaging of the Moon by Kaguya Lunar Radar Sounder Off Nadir
           Echo Data
    • Authors: Takao Kobayashi;Jung-Ho Kim;Seung Ryeol Lee;
      Pages: 3709 - 3714
      Abstract: HF (5MHz) imaging of lunar surface was attempted using off nadir echo data of Kaguya Lunar Radar Sounder (LRS). LRS observation data of multiple orbits were processed and mapped onto a surface which was defined based on the Kaguya Digital Elevation Model. The transmitting/receiving antenna of LRS is a dipole antenna which illuminates lunar surface on both sides of orbit, consequently, in a single-orbit observation, a detected target location has ambiguity in terms of the side of the orbit. However, use of multiple orbit data enables to resolve this ambiguity problem, i.e., radar illumination is controlled. We demonstrated this HF imaging technique by reconstructing surface images of Rupes Recta region using LRS observation data of 61 orbits. Control of radar illumination was confirmed by the presence/absence of Rupes Recta image in the reconstructed surface images. As it was anticipated, the reconstructed surface images presented false images which were identified as mirror images of major surface features. We also carried out simulation of these LRS observations of 61 orbits over the Rupes Recta site using Kirchhoff-approximation Surface Scattering (KiSS) simulation code. Comparison of the images of LRS observation and those of KiSS simulation exposed some discrepancies. Our interpretation is that the discrepancies are attributed to shallow subsurface scatterings which the KiSS simulation does not take into account. This implies the possibility of imaging shallow lava tubes by LRS, although we did not find one in this particular site of Rupes Recta region.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-,
           C-, and X-Band SAR With Validation by Airborne Measurements
    • Authors: Suman Singha;Malin Johansson;Nicholas Hughes;Sine Munk Hvidegaard;Henriette Skourup;
      Pages: 3715 - 3734
      Abstract: In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • LADAR System and Algorithm Design for Spectropolarimetric Scene
    • Authors: Richard K. Martin;Christian Keyser;Luke Ausley;Michael Steinke;
      Pages: 3735 - 3746
      Abstract: We present a new active imaging architecture that enables rapid spectropolarimetric imaging in a compact system. The transmitter laser produces a synchronous cascade of closely spaced wavelengths, each of which is modulated with a unique amplitude pattern. Temporal multiplexing of the optical return signals is used to reduce system cost, size, weight, and power. This architecture enables a single laser and a single detector for all wavelengths and polarization states. This in turn enables pixel-by-pixel scene characterization, which could be used for target identification in future work. The basic hardware and software architecture will be presented in addition to technologies that are being investigated to develop this system architecture. We will introduce analytical expressions for the temporally multiplexed transmitted and detected signals based on the proposed hardware configuration. We then derive optimal and computationally efficient algorithms for the estimation of the overall range per pixel and the reflectivity per wavelength per pixel, as well as the Cramer-Rao lower bound on estimator variance. The bound and simulated performance yield guidelines for the system parameters required to achieve a desired level of fidelity of the spectral and polarimetric reflectivity. The proposed system is validated with laboratory data and explored via simulations.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • BASO: A Background-Anomaly Component Projection and Separation Optimized
           Filter for Anomaly Detection in Hyperspectral Images
    • Authors: Shizhen Chang;Bo Du;Liangpei Zhang;
      Pages: 3747 - 3761
      Abstract: Many hyperspectral anomaly detectors are designed based on the traditional Mahalanobis distance-based RX algorithm, which is usually considered as an inverse operation of the principal components analysis. Such detectors include the uniform target detector (UTD) algorithm, RX-UTD algorithm, and so on. However, the possibility of background statistical contamination caused by anomalies still exists. In order to alleviate this problem, in this paper, we propose a spectral matched filter (background-anomaly component projection and separation optimized filter) to minimize the average output energy of separate image components and the output values of the weighted background regular term for hyperspectral image anomaly detection, which could strengthen the separation between anomalies and backgrounds. By calculating the optimal solution to the background-anomaly component projection and separation function, we obtain the optimal projection, where we can effectively suppress the background while highlighting the anomalies. Proposed algorithm has the following research advantages: 1) it creates a novel collaborative component projection and robust background optimization function to separate the background and anomalies and 2) it analyzes the intrinsic statistical distribution of pixels and applies appropriate iterative shrinkage-thresholding algorithm to solve the ℓ1-min problem. Experiments were conducted on three real hyperspectral data sets. The detection results demonstrate that the proposed algorithm is superior to other state-of-the-art anomaly detection algorithms.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Resolution Analysis of Spatial Modulation Coincidence Imaging Based on
           Reflective Surface
    • Authors: Yuchen He;Shitao Zhu;Guoxiang Dong;Songlin Zhang;Anxue Zhang;Zhuo Xu;
      Pages: 3762 - 3771
      Abstract: The spatial modulation coincidence imaging (SMCI), as a novel kind of microwave coincidence imaging method, is proposed in this paper. The SMCI system provides a new way to produce the time-space independent signal instead of multitransmitter architecture with wideband randomly modulated signal in radar coincidence imaging. Due to some special features, metamaterial plate is utilized as the reflective surface to modulate the incident signal to construct random radiation field. The resolution of SMCI system is analyzed under large viewing angle with two different transmitting signals. Reflective surface is nonuniformly divided to derive the expression of resolution. The analysis results show that the resolution of SMCI system is mainly determined by the size of reflective surface and center frequency, which is similar to the traditional aperture. The SMCI system is low cost and flexible in design. Simultaneously, it can avoid the synchronization problem between subsources. Moreover, the SMCI system can achieve the resolution of space target through single transmitter-single receiver radar system. High-resolution image can be reconstructed since the tests are nonlinear. Finally, a series of simulation experiments is presented based on the nondirect-viewing scene we proposed. Using the algorithm based on a compressed sensing theory, we reconstructed the target image with high resolution.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Target Reconstruction Based on 3-D Scattering Center Model for Robust SAR
    • Authors: Baiyuan Ding;Gongjian Wen;
      Pages: 3772 - 3785
      Abstract: This paper proposes a robust synthetic aperture radar (SAR) automatic target recognition method based on the 3-D scattering center model. The 3-D scattering center model is established offline from the CAD model of the target using a forward method, which can efficiently predict the 2-D scattering centers as well as the scattering filed of the target at arbitrary poses. For the SAR images to be classified, the 2-D scattering centers are extracted based on the attributed scattering center model and matched with the predicted scattering center set using a neighbor matching algorithm. The selected model scattering centers are used to reconstruct an SAR image based on the 3-D scattering center model, which is compared with the test image to reach a robust similarity. The designed similarity measure comprehensively considers the image correlation between the test image and the model reconstructed image and the model redundancy as for describing the test image. As for target recognition, the model with the highest similarity is determined to the target type of the test SAR image when it is denied to be an outlier. Experiments are conducted on both the data simulated by an electromagnetic code and the data measured in the moving and stationary target acquisition recognition program under standard operating condition and various extended operating conditions to validate the effectiveness and robustness of the proposed method.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Assessment of the SMAP Soil Emission Model and Soil Moisture Retrieval
           Algorithms for a Tibetan Desert Ecosystem
    • Authors: Donghai Zheng;Rogier van der Velde;Jun Wen;Xin Wang;Paolo Ferrazzoli;Mike Schwank;Andreas Colliander;Rajat Bindlish;Zhongbo Su;
      Pages: 3786 - 3799
      Abstract: The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature (TBp) measurements at horizontal (TBH) and vertical (TBV ) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured THB (≈ 15 K), and the TBV is underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the TBp underestimation (>5 K). The remaining TBH underestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the TBp measurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the TBp simulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the TBV measurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of TBV underestimation. The products based on the SCA with TBH measurements underestimate the SM due to underestimation of TBH, and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in TBp simulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted so-l temperature and texture information for Tibetan desert ecosystem.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Joint Margin, Cograph, and Label Constraints for Semisupervised Scene
           Parsing From Point Clouds
    • Authors: Jie Mei;Liqiang Zhang;Yuebin Wang;Zidong Zhu;Huiqian Ding;
      Pages: 3800 - 3813
      Abstract: To parse large-scale urban scenes using the supervised methods, a large amount of training data that can account for the vast visual and structural variance of urban environment is necessary. Unfortunately, such training data are mostly obtained by tedious and time-consuming manual work. To overcome the drawback, we propose a semisupervised learning framework that combines the margin, cograph, and label constraints into an objective function for point cloud parsing. Mathematically, the margin constraint is presented to learn a novel distance criterion that can effectively recognize points of different classes. The graph regularization is then employed to characterize the intrinsic geometry structure of the data manifold and explore relationships among points. The label consistency regularization is introduced to ensure the category consistency of the clustered points and single point. To classify the out-of-sample data, the framework successfully transforms the semisupervised classification results into the linear classifier by adopting a linear regression. An iterative algorithm is utilized to efficiently and effectively optimize the objective function with characteristics of multiple variables and highly nonlinear. The point clouds of four urban scenes are used to validate our method. The experimental results show that our method outperforms the state-of-the-art algorithms.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • A Coarse-to-Fine Autofocus Approach for Very High-Resolution Airborne
           Stripmap SAR Imagery
    • Authors: Jincheng Li;Jie Chen;Pengbo Wang;Otmar Loffeld;
      Pages: 3814 - 3829
      Abstract: An autofocus operation is an indispensable procedure to obtain well-focused images for synthetic-aperture radar (SAR) systems without precise navigation devices. Three challenges have been faced in the very high-resolution (VHR) airborne SAR autofocus due to the long cumulative time: the varying along-track velocity, the residual range cell migration (RCM), and the range-dependent phase errors with higher order components. When it comes to the stripmap mode, the autofocus becomes more complicated, since the scenario with a few strong scatterers is more likely to be encountered with a moving beam. Combining the merits of parametric and nonparametric autofocus algorithms, a robust motion error estimation method is proposed in this paper. First, we perform a stripmap multiaperture mapdrift autofocus operation to extract the along-track velocity and the most range-invariant errors, removing the residual RCM at a subaperture scale. Second, one referential center block is selected to retrieve the residual range-invariant error, which can eliminate the residual RCM globally in the range dimension. With a global high-quality input, the residual range-variant phase errors can be retrieved precisely utilizing a center-to-edge local maximum-likelihood weighted phase gradient autofocus kernel at last. Experiments on real VHR airborne stripmap SAR data are performed to demonstrate the robustness of the proposed method.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Experimental Analysis on the Mechanisms of Singular Point Generation in
           InSAR by Employing Scaled Optical Interferometry
    • Authors: Shunichiro Fujinami;Ryo Natsuaki;Kazuhide Ichikawa;Akira Hirose;
      Pages: 3830 - 3837
      Abstract: Interferograms obtained in interferometric synthetic aperture radar (InSAR) often suffer from decorrelation and singular points (SPs) originating from thermal noise and interference. To analyze the phenomenon, first, this paper presents the results of scaled optical experiment free from thermal noise, where the SP origin is interference. We find that the amplitude of the SP-constructing pixels, namely, singular unit, and of nearby pixels is lower than that of other pixels. This amplitude reduction is enhanced by multilooking process. These results suggest that the number of effective scatterers in a single pixel has reduced to such an extent that individual interference has become visible. We also conduct the same analysis on the SAR data. We find that plain areas show the same features as the optical experiment, implying the same mechanisms of SP generation. In contrast, sea areas present no localization, indicating thermal noise in electronics as the major reason. It is widely known that interference among many incoherent scattered waves presents Rayleigh or similar distribution in its amplitude as a result of central limit theorem. As the number of scatterers reduces, the amplitude becomes log-normal or other distribution. However, no analysis was reported on the local properties in such a case that the central limit theorem does not hold. Investigation of such local properties will also be useful in designing SP filters. The significance of noncentral-limit-theorem situations will increase its importance in the use of SAR data, of which resolution becomes further higher in the near future.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Hyperspectral Image Classification With Imbalanced Data Based on
           Orthogonal Complement Subspace Projection
    • Authors: Jiaojiao Li;Qian Du;Yunsong Li;Wei Li;
      Pages: 3838 - 3851
      Abstract: Conventional classification algorithms have shown great success for balanced classes. In remote sensing applications, it is often the case that classes are imbalanced. This paper proposes a novel solution to solve the problem of imbalanced training samples in hyperspectral image classification. It consists of two parts: one is for large-size sample sets and the other is for small-size sets. Specifically, an algorithm based on the orthogonal complement subspace projection (OCSP) is proposed to select samples from large-size classes, and an algorithm also based on OCSP is proposed to create artificial samples for small-size ones. The impact on representation-based classifiers, i.e., sparse and collaborative representation classifiers and traditional classifiers (e.g., support vector machine), is investigated. Experimental results demonstrate that the proposed solution can outperform other existing solutions in the literature.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Characterization of L-Band MIMP SAR Data From Rice Paddies at Late
           Vegetative Stage
    • Authors: Motofumi Arii;Hiroyoshi Yamada;Masato Ohki;
      Pages: 3852 - 3860
      Abstract: Insufficient validation for polarimetric decomposition techniques has led to its less perceived popularity for more than 20 years. The true composition ratio of scattering mechanisms within a radar backscatter should be essential to make polarimetric synthetic aperture radar (SAR) operational applications. To achieve this, a novel comprehensive approach to accurately identify the contribution of each scattering mechanism by a multi-incidence angle and multipolarimetric (MIMP) SAR observation combined with a theoretical model simulation is newly applied to L-band SAR data. Rice paddies in Niigata, Japan having a simple vegetation structure without topography were observed by Polarimetric and Interferometric Airborne SAR L-band 2, by gradually varying the flight path in terms of incidence angle. In addition to the MIMP SAR observation, a dominant scattering mechanism is reliably isolated through the theoretical characterization of the data by a discrete scatterer model. Avoiding unnecessary Bragg scattering effect caused by the methodically distributed rice paddies, the volume scattering from grains is identified as a dominant scattering mechanism over incidence angles. In addition, HH and VV are strongly affected by the double-bounce scattering between stalks and the ground surfaces at only small incidence angles, whereas the contribution of the double-bounce scattering for HV is not obvious. The results at the L-band will be compared with another MIMP SAR data at the X-band obtained for the same rice paddies in 2014 so that multifrequency MIMP SAR data analysis shall be conducted for our next step.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Hybrid Unmixing Based on Adaptive Region Segmentation for Hyperspectral
    • Authors: Xiangrong Zhang;Jingyan Zhang;Chen Li;Cai Cheng;Licheng Jiao;Huiyu Zhou;
      Pages: 3861 - 3875
      Abstract: Unmixing is an important issue of hyperspectral images. Most unmixing methods adopt linear mixing models for simplicity. However, multiple scattering usually occurs between vegetation and soil in a bilinear scene. Thus, nonlinear mixing problems which are difficult to be solved should be taken into consideration under this circumstance. In practice, both linear and nonlinear spectral mixtures exist in hyperspectral scenes. Considering the characteristics of different regions in images, we propose a hybrid unmixing algorithm for hyperspectral images based on region adaptive segmentation. Our method uses a standard K-means clustering algorithm to obtain different regions, including homogeneous regions and detailed regions. The model of the homogeneous regions is assumed to be linear, which will be pursued using the method of sparse-constrained nonnegative matrix factorization (NMF), and the mixing in the detailed regions is assumed to be based on a nonlinear model. We also propose a new nonlinear unmixing method, called graph-regularized semi-NMF, which considers the manifold structure of hyperspectral data as the unmixing method to deal with the detailed regions. Finally, by combining the two regions, we obtain the abundance of the whole hyperspectral image. The proposed method can not only achieve more precise abundance but also be good at keeping the edge information of the bilinear abundance. The experimental results on both synthetic and real data also show that the proposed method is effective for improving the unmixing accuracy of hyperspectral remote-sensing images.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • A Generalized Evaluation Scheme for Comparing Temperature Products from
           Satellite Observations, Numerical Weather Model, and Ground Measurements
           Over the Tibetan Plateau
    • Authors: Xiaoying Ouyang;Dongmei Chen;Yonghui Lei;
      Pages: 3876 - 3894
      Abstract: Ground surface temperature (GST) measurements are scarce in Tibetan plateau (TP), whereas the satellite observations and numerical weather model outputs are good alternatives to fill the spatial gaps among ground stations. However, the evaluation of different temperature products is challenging due to the distinct temporal and spatial dimensions in their acquisition methods. This paper intended to develop an evaluation framework for comparing the performances of various temperature data, including the Advanced Along-Track Scanning Radiometer (AATSR) satellite land surface temperature (LST) data, the high Asia refined (HAR) analysis numerical outputs, and the GST. In the proposed framework, we introduce a diurnal temperature cycle model and an aggregated weighted method to solve the temporal and spatial mismatch problem between different data sets. The results over TP show that the evaluation framework solves the temporal and spatial matching among different data sets. AATSR LST and HAR outputs are consistent regardless of the heterogeneous and weather conditions at Linzhi site indicating that the fully homogeneous land surface conditions are not the only way for the satellite/simulation validations. Our results suggest that the proposed framework of time normalization and spatial aggregation method is appropriate for evaluating satellite thermal infrared retrieved data sets and numerical simulations even when the proper ground measurements are insufficient. Since it performs well in the high elevation and complex land surface-conditioned TP region, it will be easy to be adopted in the other regions with a variety of data sets.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Evaluation of Eccentered Electrode-Type Resistivity Logging in Anisotropic
           Geological Formations With a Matrix Method
    • Authors: Guanglong Xing;Hongyu Xu;Fernando L. Teixeira;
      Pages: 3895 - 3902
      Abstract: A robust and succinct matrix-based method is developed to simulate the response of eccentered resistivity logging tools in anisotropic geological formations. This is done by first deriving the solution of the state vector describing the potential and the current component along the radial direction in a cylindrical system. Rescaled cylindrical eigenfunctions are employed to overcome the poor scaling inherent to the canonical cylindrical functions for very small or very large arguments. A Levin-type method for approximating integrals with rapidly oscillatory functions is introduced to effectively perform the numerical integrals. The influence of both the translational and rotational eccentricities of the logging tool within the borehole surrounded by an anisotropic formation is studied based on numerical results provided by the present matrix method. We found that neither translational nor rotational eccentricity can be neglected, especially in low-resistive or anisotropic geological formations.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Modeling Anisotropic Reflectance Over Composite Sloping Terrain
    • Authors: Dalei Hao;Jianguang Wen;Qing Xiao;Shengbiao Wu;Xingwen Lin;Dongqin You;Yong Tang;
      Pages: 3903 - 3923
      Abstract: Heterogeneous terrain significantly complicates signals received by airborne or satellite sensors. It has been demonstrated that both solar direct beam and diffuse skylight illumination conditions are significant factors influencing the anisotropy of reflectance over mountainous areas. Several models and methods have been developed to account for topographic effects on surface reflectance at the pixel level in remote sensing. However, subtopographic effects are generally neglected for low-spatial-resolution pixels due to the complex law of radiative transfer and the limitations of higher spatial resolution digital elevation models, which can lead to deviations in reflectance estimation. Accurately estimating the subtopographic effects on anisotropic reflectance over composite sloping terrain under different illumination conditions presents a challenge for remote sensing models and applications. In this paper, the diffused equivalent slope model (dESM) was developed, which is an anisotropic reflectance simulation model coupled with diffuse skylight over composite sloping terrain. The corresponding subtopographic impact factor was also proposed to exhibit how microslope topography affects reflectance over composite sloping terrain under different illumination conditions. Simulated reflectance data sets simulated by the radiosity method and Moderate Resolution Imaging Spectroradiometer reflectance data were used to evaluate the performance of the dESM model. The results reveal that the dESM model can accurately capture the reflectance anisotropy over composite sloping terrain under different illumination conditions, and the subtopographic impact factor can account for the effects of microslope topography, shadow, and illumination conditions.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Atmospheric Column Water Vapor Retrieval From Hyperspectral VNIR Data
           Based on Low-Rank Subspace Projection
    • Authors: N. Acito;M. Diani;
      Pages: 3924 - 3940
      Abstract: The knowledge of atmospheric column water vapor concentration is crucial for compensating water absorption effects in remote sensing data. Several algorithms for the estimation of such a parameter were proposed in the past. One of the most effective algorithms is the atmospheric precorrected differential absorption (APDA) technique. APDA relies on a simplified radiative transfer model (RTM) that does not account for the spatial variability of the adjacency effects. In this paper, we study the impact of the simplified RTM assumption on the performance of the algorithm by exploiting a more realistic and well-established RTM. Starting from such a model, we derive a new water retrieval algorithm called low-rank subspace projection-based water estimator. It exploits the high degree of spectral correlation experienced in the reflectances of most of the existing materials. An extensive experimental analysis is carried out on simulated data in order to assess and compare the performance of the two algorithms. Simulation results allow the critical analysis of the two algorithms by highlighting their strengths and drawbacks.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Indoor Person Identification Using a Low-Power FMCW Radar
    • Authors: Baptist Vandersmissen;Nicolas Knudde;Azarakhsh Jalalvand;Ivo Couckuyt;André Bourdoux;Wesley De Neve;Tom Dhaene;
      Pages: 3941 - 3952
      Abstract: Contemporary surveillance systems mainly use video cameras as their primary sensor. However, video cameras possess fundamental deficiencies, such as the inability to handle low-light environments, poor weather conditions, and concealing clothing. In contrast, radar devices are able to sense in pitch-dark environments and to see through walls. In this paper, we investigate the use of micro-Doppler (MD) signatures retrieved from a low-power radar device to identify a set of persons based on their gait characteristics. To that end, we propose a robust feature learning approach based on deep convolutional neural networks. Given that we aim at providing a solution for a real-world problem, people are allowed to walk around freely in two different rooms. In this setting, the IDentification with Radar data data set is constructed and published, consisting of 150 min of annotated MD data equally spread over five targets. Through experiments, we investigate the effectiveness of both the Doppler and time dimension, showing that our approach achieves a classification error rate of 24.70% on the validation set and 21.54% on the test set for the five targets used. When experimenting with larger time windows, we are able to further lower the error rate.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • A Modified EM Algorithm for ISAR Scatterer Trajectory Matrix Completion
    • Authors: Lei Liu;Feng Zhou;Xueru Bai;John Paisley;Hongbing Ji;
      Pages: 3953 - 3962
      Abstract: The anisotropy of radar cross section of scatterers makes the scatterer trajectory matrix incomplete in sequential inverse synthetic aperture radar images. As a result, factorization methods cannot be directly applied to reconstruct the 3-D geometry of scatterers without additional consideration. We propose a modified expectation-maximization (EM) algorithm to retrieve the complete scatterer trajectory matrix. First, we derive the motion dynamics of the projected scatterer, which approximates an ellipse. Then, based on the estimated ellipse parameters using the known data of each scatterer trajectory, we use the Kalman filter to initialize the missing data. To address the limitations of a traditional EM, which only considers the rank-deficient characteristics of the scatterer trajectory matrix, we propose to augment EM by using both the known rank-deficient and elliptical motion characteristics. Experimental results on simulated data verify the effectiveness of the proposed method.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Focusing of Medium-Earth-Orbit SAR Using an ASE-Velocity Model Based on
           MOCO Principle
    • Authors: Jianlai Chen;Mengdao Xing;Guang-Cai Sun;Yuexin Gao;Wenkang Liu;Liang Guo;Yang Lan;
      Pages: 3963 - 3975
      Abstract: The available focusing algorithms for medium-Earth-orbit (MEO) SAR are all based on the complex nonhyperbolic range equation, which may make it more difficult in imaging processing. In this paper, we model the range equation as the standard hyperbolic form based on the motion compensation (MOCO) principle. However, the conventional two-step MOCO may introduce azimuth spectrum expansion due to the potential large motion error, which can lead to severe azimuth ambiguity. To resolve this problem, we develop an omega-K algorithm based on a modified two-step MOCO and an adaptively straight equivalent (ASE)-velocity model. The algorithm is implemented through three-step processing: 1) the modified two-step MOCO does not compensate for the quadratic motion error (the main factor for the spectrum expansion); 2) an ASE-velocity model is introduced to compensate for the quadratic motion error; and 3) an extended Stolt mapping is proposed to perform the accurate range cell migration correction, and the tandem singular value decomposition-nonlinear chirp scaling algorithm is to correct the azimuth-variant phase error and to perform the azimuth compression. Processing of simulated data and airborne SAR real data validates the effectiveness of the proposed algorithm.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • 3-D Scattering Characterization of Agricultural Crops at C-Band Using SAR
    • Authors: Hannah Joerg;Matteo Pardini;Irena Hajnsek;Konstantinos P. Papathanassiou;
      Pages: 3976 - 3989
      Abstract: The aim of this paper is to interpret and characterize the changes of the 3-D polarimetric scattering signatures of agricultural crops at C-band and to relate them to temporal changes of the soil and plant parameters. For this, a time series of multibaseline (MB) synthetic aperture radar (SAR) data acquired at C-band by the airborne F-SAR system of the German Aerospace Center over the Wallerfing test site in Germany was analyzed. The availability of MB SAR data enables the resolution of scattering contributions in height by means of SAR tomography. The tomographic profiles at different polarizations were analyzed regarding temporal changes for different crop types. First, it was investigated if the center of mass (CoM) of the vertical reflectivity profiles as a single parameter enables the tracking of changes in soil and vegetation. The results show that the vertical reflectivity profiles and their CoM do not allow resolving the ambiguity if a change originates from soil or vegetation dynamics as expected. Thus, the scattering contributions from ground and volume were separated in height, using a filtering approach, and used for the estimation of the ground and volume scattering powers by means of covariance matching. Comparing the outputs with coincident ground measurements showed that dielectric as well as geometric changes in the vegetation are traceable by the separated ground and volume powers. Finally, the estimated powers were analyzed with respect to orientation effects, i.e., to polarimetric anisotropic behavior. They were found to be not significant for the crops under study at C-band.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • A Statistical Variable Selection Solution for RFM Ill-Posedness and
           Overparameterization Problems
    • Authors: Sayyed Hamed Alizadeh Moghaddam;Mehdi Mokhtarzade;Amin Alizadeh Naeini;AliReza Amiri-Simkooei;
      Pages: 3990 - 4001
      Abstract: Parameters of a rational function model (RFM), known as rational polynomial coefficients, are commonly redundant and highly correlated, leading to the problems of overparameterization and ill-posedness, respectively. In this paper, an innovative two-stage statistical method, called an uncorrelated and statistically significant RFM (USS-RFM), is presented to deal directly with these two problems. In the first stage, the proposed method employs a novel correlation analysis, which aims to exclude highly correlated coefficients. In the second stage, a new iterative significance test is applied to detect and remove unnecessary coefficients from the RFM. The proposed method is implemented on eight real data sets captured by Cartosat-1, GeoEye-1, Pleiades, Spot-3, and WorldView-3 platforms. The results are evaluated in terms of the positioning accuracy, model degrees of freedom, processing time, and figure condition analysis. Experimental results prove the efficiency of the proposed method, showing that it could achieve subpixel accuracy even for cases with five ground control points. The proposed USS-RFM is compared to an ℓ1-norm regularization (L1R) technique and a particle swarm optimization (PSO) algorithm in the terrain-dependent case of the RFM. The results demonstrate the superiority of the USS-RFM, which performs better than the alternative methods in terms of positioning accuracy by more than 50% on average. Moreover, the RFMs resulted from the USS-RFM demonstrate to have higher degrees of freedom and, as a result, higher level of reliability. From the perspective of processing time, USS-RFM and L1R are similar while both are much faster than PSO.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Unsupervised Change Detection Based on Hybrid Conditional Random Field
           Model for High Spatial Resolution Remote Sensing Imagery
    • Authors: Pengyuan Lv;Yanfei Zhong;Ji Zhao;Liangpei Zhang;
      Pages: 4002 - 4015
      Abstract: High spatial resolution (HSR) remote sensing images provide detailed geometric information about land cover. As a result, it is possible to detect more subtle changes with the help of HSR images. However, due to the increased spatial resolution and the limited spectral information, it is difficult to identify the real changes only through the spectral feature of the image. To fully explore the spectral-spatial information and improve the change detection performance for HSR images, this paper proposes the hybrid conditional random field (HCRF) model, which combines the traditional random field method with an object-based technique. In the proposed method, the spectral discriminative information of a single pixel is extracted by the unary potential, which is modeled using a soft clustering method to make an initial separation of changed and unchanged pixels. The pairwise potential then considers the contextual information of adjacent pixels to favor spatial smoothing. An object term is also introduced in the HCRF model to keep the homogeneity of changed objects. By the use of these approaches, the oversmoothing problem of the random field-based methods and the detection error caused by the segmentation strategy in the object-based methods can be relieved. The proposed method was tested on three HSR image data sets and outperformed the compared state-of-the-art techniques.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Reprojection of VIIRS SDR Imagery Using Concurrent Gradient Search
    • Authors: Alexander P. Trishchenko;
      Pages: 4016 - 4024
      Abstract: The application of the gradient search method for reprojection of Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data record (SDR) imagery is described. The method is an extension of the scheme developed earlier for reprojection of Moderate Resolution Imaging Spectroradiometer (MODIS) L1B imagery. The new scheme has three important improvements: 1) the interscan and intrascan search steps are combined into a single step to save computational time; 2) one-sided (left-right, up-down) gradients are utilized to improve convergence; and 3) the use of the map projection instead of the latitude-longitude coordinate system to improve performance and robustness. The scheme is computationally very fast, employing only basic arithmetic operations and precalculated matrices of spatial gradients. An average number of iteration steps for the reprojection of mid-latitude quadruple VIIRS SDR granule is less than 1.5, i.e., the scheme usually converges in less than 2 iterations. The ambiguity in the overlapping areas due to the bow-tie effect is resolved by forcing a solution located closer to the scan line center. In addition, the accuracy of VIIRS imagery geo-location was evaluated by comparison against MODIS 250 m images. Absolute geolocation biases of the VIIRS imagery over the 1-year period from June 01, 2016 to May 01, 2017 were found on average to be within 0.004 and -0.003 of the sample size (Δ line) in the along-track direction and 0.055 and 0.035 of the sample size (Δ pixel) in the along-scan direction for bands I2 and M7, respectively. These results demonstrate the excellent geometric performance of the VIIRS Suomi National Polar-orbiting Partnership sensor and are consistent with those reported by the VIIRS geolocation teams.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Practical Multichannel SAR Imaging in the Maritime Environment
    • Authors: Robert W. Jansen;Raghu G. Raj;Luke Rosenberg;Mark A. Sletten;
      Pages: 4025 - 4036
      Abstract: The U.S. Naval Research Laboratory (NRL) recently developed an X-band airborne multichannel synthetic aperture radar (MSAR) test bed system that consists of 32 along-track phase centers. This system was deployed in September 2014 and again in October 2015 to perform extensive and systematic data collections on a variety of land and maritime scenes under different environmental conditions. This paper presents a detailed experimental analysis of imaging in the maritime domain using data captured by the NRL MSAR system. After presenting some of the important details of our NRL MSAR system, we demonstrate velocity-based imaging of a variety of moving backscatter sources including ships and shoaling ocean waves. Our analysis is based on the velocity SAR (VSAR) technique, which was originally conceived by Friedlander and Porat. Practical application of this algorithm in the maritime domain requires a number of pre- and postprocessing stages, which are described here in detail. Our results are then benchmarked against the traditional along-track interferometry, where it is demonstrated that VSAR processing is better able to correctly compensate motion-induced distortion.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Wavenumber-Domain Multiband Signal Fusion With Matrix-Pencil Approach for
           High-Resolution Imaging
    • Authors: Jianping Wang;Pascal Aubry;Alexander Yarovoy;
      Pages: 4037 - 4049
      Abstract: In this paper, a wavenumber-domain matrix-pencil-based multiband signal fusion approach was proposed for multiband microwave imaging. The approach proposed is based on the Born approximation of the field scattered from a target resulting in the fact that in a given scattering direction, the scattered field can be represented over the whole frequency band as a sum of the same number of contributions. Exploiting the measured multiband data and taking advantage of the parametric modeling for the signals in a radial direction, a unified signal model can be estimated for a large bandwidth in the wavenumber domain. It can be used to fuse the signals at different subbands by extrapolating the missing data in the frequency gaps between them or coherently integrating the overlaps between the adjacent subbands, thus synthesizing an equivalent wideband signal spectrum. Taking an inverse Fourier transform, the synthesized spectrum results in a focused image with improved resolution. Compared with the space-time domain fusion methods, the proposed approach is applicable for radar imaging with the signals collected by either collocated or noncollocated arrays in different frequency bands. Its effectiveness and accuracy are demonstrated through both numerical simulations and experimental imaging results.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Exploiting Structured Sparsity for Hyperspectral Anomaly Detection
    • Authors: Fei Li;Xiuwei Zhang;Lei Zhang;Dongmei Jiang;Yanning Zhang;
      Pages: 4050 - 4064
      Abstract: Sparse representation-based background modeling facilitates much recent progress in hyperspectral anomaly detection (AD). The sparse representation of background often exhibits underlying structure, which is crucial to distinguish between background and anomaly. However, how to exploit such underlying structure is still challenging. To address this problem, we present a novel hyperspectral AD method, which can exploit the structured sparsity in modeling the background more accurately. With the plausible background area detected by a local RX detector, a robust background spectrum dictionary is learned in a principal component analysis way. A reweighted Laplace prior-based structured sparse representation model is then employed to reconstruct the spectrum of each pixel. With considering the structured sparsity in representation, the background pixels can be reconstructed more accurately than the anomaly ones, which thus can be detected based on the reconstruction error. To further improve the detection performance, an intracluster reconstruction model is developed to exploit the spatial similarity among the background pixels in the same cluster. The anomaly pixels can then be detected based on the cost of intracluster reconstruction error. By linearly combining these two detection results, improvement is obviously achieved on detection accuracy. Experimental results on both simulated and real-world data sets demonstrate that the proposed method outperforms several state-of-the-art hyperspectral AD methods.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Relating GPM Radar Reflectivity Profile Characteristics to Path-Integrated
    • Authors: Stephen L. Durden;
      Pages: 4065 - 4074
      Abstract: The Global Precipitation Measurement (GPM) mission was launched in February 2014; its dual-frequency precipitation radar (DPR) operates at both Ku- and Ka-band s. Attenuation in precipitation is typically not negligible, especially at Ka-band. Hence, attenuation correction is an important part of the GPM DPR retrieval algorithm. The operational algorithm uses a path-integrated attenuation (PIA) obtained by comparing the measured surface return with that expected either from a nearby, nonprecipitating area or from the same area, acquired at a previous, nonprecipitating time. This surface reference technique has worked well in most situations but can result in erroneously low estimates of the path attenuation in situations with nonuniform filling of the radar beam, especially at Ka-band due to its larger attenuation. This paper explores the existence of relationships between the Ka-band PIA and the characteristics of the measured reflectivity profiles. The author finds that PIA is, indeed, related to reflectivity profiles, with strongest correlation between the PIA and the measured rainfall dual-frequency reflectivity ratio just above the surface. This relationship could be used as an estimator in the cases with severe nonuniform beam filling.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Assessment of Wind Speed Estimation From C-Band Sentinel-1 Images Using
           Empirical and Electromagnetic Models
    • Authors: Tran Vu La;Ali Khenchaf;Fabrice Comblet;Carole Nahum;
      Pages: 4075 - 4087
      Abstract: Surface wind speed estimation from synthetic aperture radar (SAR) data is principally based on empirical (EP) approaches, e.g., CMOD functions. However, it is necessary and significant to compare radar backscattering modeling based on EP and electromagnetic (EM) approaches for enhancing the understanding of the physical processes between radar signal and sea surface, which is important for the design of radar sensors (e.g., cyclone global navigation satellite system). Indeed, through comparisons, it is worth noticing that the scattering of wave breaking is not taken into account in the physical modeling of radar backscattering. Surface wind speed is selected here as a reference parameter for investigating the difference between EP and EM models, due to its important role in radar backscattering modeling. In addition, wind speed estimates can be easily compared to in situ measurements. For EP approach, CMOD5.N and Komarov's model are selected for wind speed estimation from Sentinel-1 images. The CMOD5.N can offer wind speed estimates up to 25-35 m/s, while wind speed estimation based on Komarov's model does not require wind direction input. For EM approach, the asymptotic models, i.e., composite two-scale model, small-slope approximation (SSA), and resonant curvature approximation (RCA), are investigated for wind speed retrieval. They are studied with two models of surface roughness spectrum: semi-EP spectrum and EP model. In general, normalized radar cross section (NRCS) calculated by CMOD5.N and SSA/RCA is quite similar for incidence angles below 40° in vertical polarized and below 30° in horizontal polarized. For larger ones, significant NRCS deviations between two approaches are demonstrated, due to the lack of wave breaking scattering in EM models. As a result, wind speed estimates by CMOD5.N and SSA/RCA are very close for low and moderate incidence angles, while SSA-/RCA-based wind speeds are overestimated for larger one-.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Global Ocean Altimetry With GNSS Reflections From TechDemoSat-1
    • Authors: Jake Mashburn;Penina Axelrad;Stephen T. Lowe;Kristine M. Larson;
      Pages: 4088 - 4097
      Abstract: TechDemoSat-1 (TDS-1) is an experimental Global Navigation Satellite System Reflections (GNSS-R) satellite launched in 2014. The GNSS-R receiver onboard performs real-time navigation and generates delay-Doppler correlation maps for Earth-reflected Global Positioning System (GPS) L1 C/A ranging signals. This paper investigates the performance of the TDS-1 data for ocean surface altimetry retrievals. The analysis includes consideration of the transmitter and receiver orbits, time tag corrections, models for ionospheric and tropospheric delays, zenith to nadir antenna baseline offsets, ocean and solid Earth tides, and a comparison with mean sea surface topography. An error budget is compiled to account for each error source and compared with the experimentally derived surface height retrievals. By analyzing data sets covering global ocean surfaces over ±60° latitude, the current performance of spaceborne GNSS-R altimetry with the TDS-1 data set is experimentally established. In comparison with the mean sea surface topography, the surface height residuals are found to be 6.4 m, $1sigma $ with a 1-s integration time. A discussion of the factors limiting this performance is presented, with implications for future GNSS-R altimetry missions designed for the observation of mesoscale ocean circulation.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization
    • Authors: Tatsumi Uezato;Mathieu Fauvel;Nicolas Dobigeon;
      Pages: 4098 - 4108
      Abstract: Spectral unmixing (SU) methods incorporating the spatial regularizations have demonstrated increasing interest. Although spatial regularizers that promote smoothness of the abundance maps have been widely used, they may overly smooth these maps and, in particular, may not preserve edges present in the hyperspectral image. Existing unmixing methods usually ignore these edge structures or use edge information derived from the hyperspectral image itself. However, this information may be affected by the large amounts of noise or variations in illumination, leading to erroneous spatial information incorporated into the unmixing procedure. This paper proposes a simple yet powerful SU framework that incorporates external data [i.e. light detection and ranging (LiDAR) data]. The LiDAR measurements can be easily exploited to adjust the standard spatial regularizations applied to the unmixing process. The proposed framework is rigorously evaluated using two simulated data sets and a real hyperspectral image. It is compared with methods that rely on spatial information derived from a hyperspectral image. The results show that the proposed framework can provide better abundance estimates and, more specifically, can significantly improve the abundance estimates for the pixels affected by shadows.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Efficient Phase Estimation for Interferogram Stacks
    • Authors: Homa Ansari;Francesco De Zan;Richard Bamler;
      Pages: 4109 - 4125
      Abstract: Signal decorrelation poses a limitation to multipass SAR interferometry. In pursuit of overcoming this limitation to achieve high-precision deformation estimates, different techniques have been developed, with short baseline subset, SqueeSAR, and CAESAR as the overarching schemes. These different analysis approaches raise the question of their efficiency and limitation in phase and consequently deformation estimation. This contribution first addresses this question and then proposes a new estimator with improved performance, called Eigendecomposition-based Maximum-likelihood-estimator of Interferometric phase (EMI). The proposed estimator combines the advantages of the state-of-the-art techniques. Identical to CAESAR, EMI is solved using eigendecomposition; it is therefore computationally efficient and straightforward in implementation. Similar to SqueeSAR, EMI is a maximum-likelihood-estimator; hence, it retains estimation efficiency. The computational and estimation efficiency of EMI renders it as an optimum choice for phase estimation. A further marriage of EMI with the proposed Sequential Estimator by Ansari et al. provides an efficient processing scheme tailored to the analysis of Big InSAR Data. EMI is formulated and verified in relation to the state-of-the-art approaches via mathematical formulation, simulation analysis, and experiments with time series of Sentinel-1 data over the volcanic island of Vulcano, Italy.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Precision of Vegetation Height Estimation Using the Dual-Baseline PolInSAR
           System and RVoG Model With Temporal Decorrelation
    • Authors: Hélène Sportouche;Antoine Roueff;Pascale C. Dubois-Fernandez;
      Pages: 4126 - 4137
      Abstract: Estimating vegetation height from polarimetric interferometric synthetic aperture radar (PolInSAR) data using the Random Volume over Ground model has motivated several studies. Most of these propose estimators and apply them to real data to demonstrate their potential. In previous publications on the single-baseline system, we proposed a complementary approach, which consisted in analyzing the precision of estimations of vegetation height that can be expected depending on the considered model and on the available a priori knowledge. In this paper, we develop such an analysis for the case of a dual-baseline (DB) system. We consider the DB configuration with a PolInSAR set obtained with three PolSAR acquisitions, the extinction coefficient of the volume is assumed unknown, and the level of temporal decorrelation is assumed to be unknown. The observed high sensitivity of the vegetation height Cramer-Rao bound (CRB) with respect to the system parameters and the vegetation characteristics shows that the system optimization cannot guarantee 1-m precision for all vegetation heights, even for large estimation windows with N=2000 pixels. Nevertheless, an operating regime exists for which the vegetation height estimation precision is around 1 m for N=200 pixels. This regime is obtained for a pair of wavenumbers (0.06 and 0.25 m-1), for vegetation height ranging [20, 50] m, and for polarimetric contrast between the ground and the volume larger than 0.3. Furthermore, we investigate the performance of a maximum-likelihood estimator and compare this to the precision given by the CRB. For the examples considered, with N=200 pixels, we observed convergence issues of the estimator when the polarimetric contrast is smaller or equal to 0.3.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • An Iterative Modified Diffraction Tomography Method for Reconstruction of
           a High-Contrast Buried Object
    • Authors: Maryam Hajebi;Ahad Tavakoli;Mojtaba Dehmollaian;Parisa Dehkhoda;
      Pages: 4138 - 4148
      Abstract: An iterative subsurface inverse scattering algorithm has been proposed to profile high-contrast 2-D dielectric objects buried under a lossy ground. The proposed iterative modified diffraction tomography (IMDT) method is based on the combination of the traditional iterative Born method and DT technique which is well known for its simplicity and robustness. In effect, IMDT is an iterative Born algorithm that utilizes the spectral domain concept of DT for solving the inversion problem. The proposed iterative approach results in removing the Born approximation's limitation of DT technique in dealing with high-contrast scatterers. To this end, the total field inside the reconstruction domain is renewed and then expanded into upgoing and downgoing plane waves in each iteration. Consequently, by exponential expansion of the fields and deriving a modified DT formulation, high-contrast targets are also efficiently reconstructed. To assess the proposed IMDT method, various high-contrast objects and noise conditions are studied. It is shown that the IMDT algorithm significantly outperforms the DT technique and is capable of reconstructing high-contrast objects efficiently and accurately even in noisy environments.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • A Simulation-Based Approach to Assess Subpixel Vegetation Structural
           Variation Impacts on Global Imaging Spectroscopy
    • Authors: Wei Yao;Jan van Aardt;Martin van Leeuwen;Dave Kelbe;Paul Romanczyk;
      Pages: 4149 - 4164
      Abstract: Consistent and scalable estimation of vegetation structural parameters-essential to understanding forest ecosystems-is widely investigated through remote sensing imaging spectroscopy. NASA's proposed spaceborne mission, the Hyperspectral Infrared Imager (HyspIRI), will measure spectral radiance from 380 to 2500 nm in 10-nm contiguous bands with a 60-m ground sample distance (GSD) and provide a global benchmark from which future changes can be assessed. The historic foci of spectrometers have been foliar/canopy biochemistry and species classification; however, given the relatively large GSD of a spaceborne instrument, there is uncertainty as to the effects of subpixel vegetation structure on observed radiance. This paper, therefore, evaluates the linkages between the within-pixel vegetation structure and imaging spectroscopy signals at the pixel level. We constructed a realistic virtual forest scene representing the National Ecological Observatory Network (NEON) Pacific Southwest domain site. Anticipated HyspIRI data (60-m GSD) for this site were then simulated using the physics-driven Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Both the models were first validated via comparison to overflow classic Airborne Visible/Infrared Imaging Spectrometer and NEON's imaging spectrometer (NIS). Then, to assess the impact of within-pixel: 1) tree canopy cover (CC); 2) tree positioning; and 3) distribution on large-footprint HyspIRI signals, we generated the variations of the baseline virtual forest scene and measured the anticipated spectral radiance using DIRSIG. Results indicate that HyspIRI is sensitive to subpixel vegetation structural variation in the visible to a short-wavelength infrared spectrum due to vegetation structural changes. This has implications for improving the system's suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this subpix-l variation.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Hyperspectral Image Super-Resolution Based on Spatial and Spectral
           Correlation Fusion
    • Authors: Chen Yi;Yong-Qiang Zhao;Jonathan Cheung-Wai Chan;
      Pages: 4165 - 4177
      Abstract: Super-resolution image reconstruction has been utilized to overcome the problem of spatial resolution limitation in hyperspectral (HS) imaging. To improve the spatial resolution of HS image, this paper proposes an HS-multispectral (MS) fusion method, which exploits spatial and spectral correlations and proper regularization. High spatial correlation between MS image and the desired high-resolution HS image is conserved via an over-completed dictionary, and the spectral degradation between them projected onto the space of sparsity is applied as the spectral constraint. The high spectral correlation between high-spatial- and low-spatial-resolution HS image is preserved through linear spectral unmixing. The idea of an interactive feedback proposed in our previous work is also used when dealing with spatial reconstruction and unmixing. Low-rank property is introduced in this paper to regularize the sparse coefficients of the HS patch matrix, which is utilized as the spatial constraint. Experiments on both simulated and real data sets demonstrate that the proposed fusion algorithm achieves lower spectral distortions and the super-resolution results are superior to those of other state-of-the-art methods.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Scene Capture and Selected Codebook-Based Refined Fuzzy Classification of
           Large High-Resolution Images
    • Authors: Li Yan;Ruixi Zhu;Yi Liu;Nan Mo;
      Pages: 4178 - 4192
      Abstract: Scene classification has been successfully applied to the semantic interpretation of large high-resolution images (HRIs). The bag-of-words (BOW) model has been proven to be effective but inadequate for HRIs because of the complex arrangement of the ground objects and the multiple types of land cover. How to define the scenes in HRIs is still a problem for scene classification. The previous methods involve selecting the scenes manually or with a fixed spatial distribution, leading to scenes with a mixture of objects from different categories. In this paper, to address these issues, a scene capture method using adjacent segmented images and a support vector machine classifier is proposed to generate scenes dominated by one category. The codebook in BOW is obtained from clustering features extracted from all the categories, which may lose the discrimination in some vocabularies. Thus, more discriminative visual vocabularies are selected by the introduced mutual information and the proposed intraclass variability balance in each category, to decrease the redundancy of the codebook. In addition, a refined fuzzy classification strategy is presented to avoid misclassification in similar categories. The experimental results obtained with three different types of HRI data sets confirm that the proposed method obtains classification results better than those obtained by most of the previous methods in all the large HRIs, demonstrating that the selection of representative vocabularies, the refined fuzzy classification, and the scene capture strategy are all effective in improving the performance of scene classification.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Kernel Low-Rank Multitask Learning in Variational Mode Decomposition
           Domain for Multi-/Hyperspectral Classification
    • Authors: Zhi He;Jun Li;Kai Liu;Lin Liu;Haiyan Tao;
      Pages: 4193 - 4208
      Abstract: Multitask learning (MTL) has recently yielded impressive results for classification of remotely sensed data due to its ability to incorporate shared information across multiple tasks. However, it remains a challenging issue to achieve robust classification results in the case that the data are from nonlinear subspaces. In this paper, we propose a kernel low-rank MTL (KL-MTL) method to handle multiple features from the 2-D variational mode decomposition (2-D-VMD) domain for multi-/hyperspectral classification. On the one hand, a nonrecursive 2-D-VMD method is applied to extract various features [i.e., intrinsic mode functions (IMFs)] of the original data concurrently. Compared with the existing 2-D empirical mode decomposition, 2-D-VMD has much stronger mathematical foundation and does not need any recursive sifting process. On the other hand, KL-MTL is proposed for classification by taking the extracted IMFs as features of multiple tasks. In KL-MTL, the low-rank representation formulated by nuclear norm can capture global structure of multiple tasks, while the kernel tricks are utilized for nonlinear extension of the low-rank MTL. Moreover, the optimization problem in KL-MTL is solved by the inexact augmented Lagrangian method. Compared with several state-of-the-art feature extraction and classification methods, the experimental results using both multi-/hyperspectral images demonstrate that the proposed method has satisfactory classification performance.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Ship Wake Components: Isolation, Reconstruction, and Characteristics
           Analysis in Spectral, Spatial, and TerraSAR-X Image Domains
    • Authors: Yu-Xin Sun;Peng Liu;Ya-Qiu Jin;
      Pages: 4209 - 4224
      Abstract: Based on a joint analysis of linear Kelvin wake kinematics and water dispersion relation, the features of several components of ship wake are identified in the spectral domain, such as the “X”-shaped Kelvin wake, the narrow “X”-shaped solitary wave packets, and the cross-shaped turbulent and near-field waves. Alternatively, these components can be separately reconstructed in spatial domain using the inverse Fourier transformation. These relations are verified through numerical simulation of the wakes of a ship moving at different speeds. This wake decomposition is now extended to wake feature analysis of real synthetic aperture radar (SAR) image. It reveals that although the images of ship wake have been modulated by SAR imaging mechanisms in various aspects, their spectral characteristics are closely analogous to that of wake surface elevation. Taking advantage of the loci and shape of the wake spectrum, the transverse wave, the divergent wave, the turbulent wave, and the solitary wave packets can be isolated from the original SAR image with full wake appearance. The reconstructed images of wake components facilitate the further estimation of the direction, speed, length, hull geometry, and propulsion system of the ship. This decomposition can also recover wake components from multiple ship wakes and provide an understanding of their roles on SAR image.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
  • Prelaunch Radiometric Calibration of the TanSat Atmospheric Carbon Dioxide
           Grating Spectrometer
    • Authors: Zhongdong Yang;Yuquan Zhen;Zenshan Yin;Chao Lin;Yanmeng Bi;Wu Liu;Qian Wang;Long Wang;Songyan Gu;Longfei Tian;
      Pages: 4225 - 4233
      Abstract: TanSat is an important satellite in the Chinese Earth Observation Program which is designed to measure global atmospheric CO2 concentrations from space. The first Chinese superhigh-resolution grating spectrometer for measuring atmospheric CO2 is aboard TanSat. This spectrometer is a suite of three grating spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 band near 1.61 and 2.06 μm and in the molecular oxygen A-band (O2A) at 0.76 μm. Their spectral resolving power (λ/Aλ) is ~19000, ~12800, and ~12250 in the O2A, weak absorption band of molecular carbon dioxide band, and strong absorption of carbon dioxide band, respectively. This paper describes the laboratory radiometric calibration of the spectrometer suite, which consists of measurements of the dark current response, gain coefficients, and signal-to-noise ratio (SNR). The SNRs of each channel meet the mission requirements for the O2A and weak CO2 band but slightly miss the requirements in a few channels in the strong CO2 band. The gain coefficients of the three bands have a negligible random error component and achieve very good stability. Most of the R-squared of gain coefficients model consist of five numbers of nine (e.g., 0.99999) after the decimal point, suggesting that the instrument has significant response linearity. The radiometric calibration results meet the requirements of an absolute calibration uncertainty of less than 5%.
      PubDate: July 2018
      Issue No: Vol. 56, No. 7 (2018)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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