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Journal Cover Geoscience and Remote Sensing, IEEE Transactions on
  [SJR: 1.975]   [H-I: 168]   [184 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0196-2892
   Published by IEEE Homepage  [191 journals]
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • 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: March 2018
      Issue No: Vol. 56, No. 3 (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: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Presents a listing of institutional institutions relevant for this issue of the publication.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • List of reviewers
    • Pages: 1219 - 1228
      Abstract: The publication offers a note of thanks and lists its reviewers.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Multirelay Cooperation Method for Wireless Transmission of MWD and LWD
    • Authors: Zaiping Nie;Zijian Liu;Xiangyang Sun;
      Pages: 1229 - 1237
      Abstract: Facing the challenges of the transmission attenuation in lossy formations, and the mutual interference of the waves radiated from the different relay coils and the multipath propagations of the different wave components, we proposed an efficient cooperation transmission approach based on the in-phase superposition of the multiple wave components for wireless telemetry of measurement while drilling (MWD) and logging while drilling (LWD) signals. Without any previous knowledge of the inhomogeneous formations, such as the thickness and the electrical parameters of the different layers, we propose this method which is able to achieve in-phase enhancement of the multiple components adaptively, i.e., the cooperation transmission of the MWD and LWD signals, leading to the remarkable enhancement of the field amplitude and the depression of the mutual interference of the multipath components. Therefore, the signal-to-noise ratio in a given cooperation transmission channel can be improved evidently. Some numerical examples have been given in this paper to show the remarkable improvement of the wireless transmission performance quantitatively.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Spatial Scaling Using Temporal Correlations and Ensemble Learning to
           Obtain High-Resolution Soil Moisture
    • Authors: Subit Chakrabarti;Jasmeet Judge;Tara Bongiovanni;Anand Rangarajan;Sanjay Ranka;
      Pages: 1238 - 1250
      Abstract: A novel algorithm is developed to downscale soil moisture (SM), obtained at satellite scales of 10–40 km to 1 km by utilizing its temporal correlations to historical auxiliary data at finer scales. Including such correlations drastically minimizes the size of the training set needed, accounts for time-lagged relationships, and enables downscaling even in the presence of short gaps in the auxiliary data. The algorithm is based upon bagged regression trees (BRT) and uses correlations between high-resolution remote sensing products and SM observations. The algorithm trains multiple RTs and automatically chooses the trees that generate the best downscaled estimates. The algorithm was evaluated using a multiscale synthetic data set in north central Florida for two years, including two growing seasons of corn and one growing season of cotton per year. The time-averaged error across the region was found to be 0.01 m3/m3, with a standard deviation of 0.012 m3/m3 when 0.02% of the data were used for training in addition to temporal correlations from the past seven days, and all available data from the past year. The maximum spatially averaged errors obtained using this algorithm in downscaled SM were 0.005 m3/m3, for pixels with cotton land cover. When land surface temperature (LST) on the day of downscaling was not included in the algorithm to simulate “data gaps,” the spatially averaged error increased minimally by 0.015 m3/m3 when LST is unavailable on the day of downscaling. The results indicate that the BRT-based algorithm provides high accuracy for downscaling SM using complex nonlinear spatiotemporal correlations, under heterogeneous micrometeorological conditions.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Improving MISR AOD Retrievals With Low-Light-Level Corrections for Veiling
    • Authors: Marcin L. Witek;David J. Diner;Michael J. Garay;Feng Xu;Michael A. Bull;Felix C. Seidel;
      Pages: 1251 - 1268
      Abstract: Operational retrievals of aerosol optical depth (AOD) from Multi-angle Imaging SpectroRadiometer (MISR) data have been shown to have a high bias in pristine oceanic areas. One line of evidence involves comparison with Maritime Aerosol Network (MAN) observations, including the areas of low aerosol loading close to Antarctica. In this paper, a principal reason for the AOD overestimation is identified, which is stray light measured by the MISR cameras in dark regions of high-contrast scenes. A small fraction of the light from surrounding bright areas, such as clouds or sea ice, is redistributed to dark areas, artificially increasing their brightness. Internal reflections and light scattering from optical elements in MISR’s pushbroom cameras contribute to this veiling light effect. A simple correction model is developed that relies on the average scene brightness and an empirically determined set of veiling light coefficients for each MISR camera and wavelength. Several independent methods are employed to determine these coefficients. Three sets of coefficients are further implemented and tested in prototype MISR 4.4-km AOD retrievals. The results show dramatic improvements in retrieved AODs compared against MAN observations and the currently operational V22 MISR retrievals. For the best performing set of coefficients, the bias is reduced by 51%, from 0.039 to 0.019, the RMSE is lowered by 19%, from 0.062 to 0.050, and 84% of retrievals fall within the uncertainty envelope compared with 66% of retrievals in V22. The best performing set will be implemented operationally in the next V23 MISR AOD product release.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Polarimetric Active Transponder With Extremely Large RCS for Absolute
           Radiometric Calibration of SMAP Radar
    • Authors: Kamal Sarabandi;Mani Kashanianfard;Adib Y. Nashashibi;Leland E. Pierce;Ryan Hampton;
      Pages: 1269 - 1277
      Abstract: This paper presents a new single-antenna polarimetric active radar calibrator (PARC) intended for the polarimetric and absolute radiometric calibration of the NASA’s soil moisture active passive (SMAP) radar. The PARC receives and retransmits the SMAP signal through a dual-polarized horn antenna with 17-dB gain at the center frequency of 1.26 GHz with 100 MHz of bandwidth. The transmit and receive polarizations are perpendicular and are isolated from each other using a precision orthomode transducer (OMT) specially designed for this application. The antenna is rotated 45° in the plane perpendicular to the direction of incidence, so that the scattering matrix of the PARC with respect to SMAP polarization coordinates has equal entries that enable radiometric calibration of all four channels simultaneously. As SMAP radar resolution is coarse (1 km), a point target with a very large radar cross section (RCS) is required to provide a high signal-to-clutter ratio. The proposed PARC can provide RCS values as high as 80 dBsm to achieve a 30-dB signal-to-clutter ratio. The PARC is controlled by a microcontroller to autonomously start minutes before the SMAP radar is expected to scan the area, stabilize the amplifier gain, record the magnitude of the pulses transmitted from the radar, and transmit these data to a base station. The design procedures of the OMT and the antenna, as well as control and RF circuits, are discussed, a number of leakage cancelation techniques are introduced to increase the isolation between the ports, the RCS of the fabricated PARC is fully characterized, and the image of the PARC as seen by SMAP is presented.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Polarimetric Phase and Implications for Urban Classification
    • Authors: Donald K. Atwood;Laetitia Thirion-Lefevre;
      Pages: 1278 - 1289
      Abstract: The classification of urban environments poses significant challenges for polarimetric synthetic aperture radar (SAR), since rotated urban blocks violate the assumption of reflection symmetry held by many decomposition approaches. Vast sections of cities appear to be dominated by volume scattering due to the introduction of coherent depolarization. Attempts to model this slanted double bounce must contend with the fact that dihedrals rotated about the vertical axis do not support significant backscatter and require a rough ground surface to affect a return path. In this paper, the concept of an effective dihedral is introduced, with one plate coincident with the building wall and one plate associated with some ground facet, oriented so as to support double bounce. The rotation of this effective dihedral can be readily related to the polarization orientation angle and used to quantify co-pol and cross-pol phases. Theoretical results are confirmed with L-band and C-band polarimetric SAR imagery of San Francisco, as well as by broadband laboratory experiments with modeled buildings. In addition, the utility of using cross-pol phase to classify urban regions in the Los Angeles basin is demonstrated with ascending and descending dual-pol Radarsat data.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Design of a Generic 3-D Scene Generator for Passive Optical Missions and
           Its Implementation for the ESA’s FLEX/Sentinel-3 Tandem Mission
    • Authors: Carolina Tenjo;Juan Pablo Rivera-Caicedo;Neus Sabater;Jorge Vicent Servera;Luis Alonso;Jochem Verrelst;José Moreno;
      Pages: 1290 - 1307
      Abstract: During the design phase of a satellite mission, end-to-end mission performance simulator (E2ES) tools allow scientists and engineers evaluating the mission concept, consolidating system technical requirements and analyzing the suitability of the implemented technical solutions and data processing algorithms. The generation of synthetic scenes is one of the core parts of an E2ES, providing scenes (ground truth) as would be observed by satellite instruments and used as reference against simulated retrieved mission products. An appropriate generation of the scene also allows assessing the performance of the ground data processing chain replacing real instrument data before the mission is in orbit, for which the fidelity of the scene generation is critical. This paper describes the design of a generic scene generator (GSG) with capabilities to generate complex 3-D synthetic scenes that combine the effects of surface, heterogeneity, topography and atmosphere, and viewing/illumination geometry. The proposed design allows generating consistent high spatial and spectral resolution top-of-atmosphere radiance scenes for multiple instruments based on the use of thematic maps, radiative transfer models, and reflectance databases. The described GSG was implemented within the FLuorescence EXplorer (FLEX) E2ES software tool and showed its capabilities to generate compatible scenes for the fluorescence imaging spectrometer, ocean and land color instrument, and sea and land surface temperature radiometer instruments of ESA’s FLEX/Sentinel-3 tandem mission and to validate the fulfillment of the FLEX mission requirements.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Sensor-Driven Hierarchical Method for Domain Adaptation in
           Classification of Remote Sensing Images
    • Authors: Claudia Paris;Lorenzo Bruzzone;
      Pages: 1308 - 1324
      Abstract: This paper presents a sensor-driven hierarchical domain adaptation method that aims at transferring the knowledge from a source domain (RS image where reference data are available) to a different but related target domain (RS image where no labeled reference data are available) for solving a classification problem. Due to the different acquisition conditions, a difference in the source and target distributions of the features representing the same class is generally expected. To solve this problem, the proposed method takes advantage from the availability of multisensor data to hierarchically detect features subspaces where for some classes data manifolds are partially (or completely) aligned. These feature subspaces are associated with invariant physical properties of classes measured by the sensors in the scene, i.e., measures having almost the same behavior in both domains. The detection of these invariant feature subspaces allows us to infer labels of the target samples that result more aligned to the source data for the considered subset of classes. Then, the labeled target samples are analyzed in the full feature space to classify the remaining target samples of the same classes. Finally, for those classes for which none of the sensors can measure invariant features, we perform the adaptation via a standard active learning technique. Experimental results obtained on two real multisensor data sets confirm the effectiveness of the proposed method.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Pitfalls in GPR Data Interpretation: False Reflectors Detected in Lunar
           Radar Cross Sections by Chang’e-3
    • Authors: Chunlai Li;Shuguo Xing;Sebastian E. Lauro;Yan Su;Shun Dai;Jianqing Feng;Barbara Cosciotti;Federico Di Paolo;Elisabetta Mattei;Yuan Xiao;Chunyu Ding;Elena Pettinelli;
      Pages: 1325 - 1335
      Abstract: Chang’e-3 (CE-3) has been the first spacecraft to soft land on the moon since the Soviet Union’s Luna 24 in 1976. The spacecraft arrived at Mare Imbrium on December 14, 2013, and the same day, Yutu lunar rover separated from lander to start its exploration of the surface and the subsurface around the landing site. The rover was equipped, among other instruments, with two lunar penetrating radar systems having a working frequency of 60 and 500 MHz. The radars acquired data for about two weeks while the rover was slowly moving along a path of about 114 m. At navigation point N0209, the rover got stacked into the lunar soil and after that only data at a fixed position could be collected. The low-frequency radar data have been analyzed by different authors and published in two different papers, which reported totally controversial interpretations of the radar cross sections. This paper is devoted to resolve such controversy by carefully analyzing and comparing the data collected on the moon by Yutu rover and on earth by a prototype of LPR mounted onboard a model of the CE-3 lunar rover. Such analysis demonstrates that the deep radar features previously ascribed to the lunar shallow stratigraphy are not real reflectors, rather they are signal artifacts probably generated by the system and its electromagnetic interaction with the metallic rover.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Contextual Online Dictionary Learning for Hyperspectral Image
    • Authors: Wei Fu;Shutao Li;Leyuan Fang;Jón Atli Benediktsson;
      Pages: 1336 - 1347
      Abstract: Sparse representation (SR) has been successfully used in the classification of hyperspectral images (HSIs) by representing HSI pixels over a dictionary and yielding discriminative sparse coefficients. Most of SR-based classification methods construct the dictionary by directly using some labeled pixels as atoms. Such dictionary can lead to inefficient SR for large-sized HSIs, and may be incomplete when the number of labeled pixels is less than the number of spectral bands. This paper proposes a contextual online dictionary learning (DL) method for HSIs classification, which learns a dictionary over the whole image rather than few labeled pixels. The proposed method can effectively and efficiently improve the adaptive representation capability of different pixels with an online learning mechanism. Specifically, the contextual characteristics of the HSI are integrated with discriminative spectral information for online DL, i.e., pushing similar pixels in neighborhood to share similar sparse coefficients with respect to the well-learned dictionary. By this way, the obtained sparse coefficients are structured and discriminative. Finally, a traditional classifier, i.e., the linear support vector machine, is applied to the sparse coefficients, and the final classification results are obtained. Experimental results on real HSIs show the effectiveness of the proposed method.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Robust Sparse Unmixing for Hyperspectral Imagery
    • Authors: Dan Wang;Zhenwei Shi;Xinrui Cui;
      Pages: 1348 - 1359
      Abstract: A linear sparse unmixing method based on spectral library has been widely used to tackle the hyperspectral unmixing problem, under the assumption that the spectrum of each pixel in the hyperspectral scene can be expressed as a linear combination of pure endmembers in the spectral library. However, because of the ion (atom) substitution in the geological process, there often exists spectral variability between the measured endmembers in the real environment and corresponding ones in the spectral library, which poses a significant challenge to linear sparse unmixing. Physically, the substitution leads to the variation of absorption peaks of endmembers, making the spectral variation of sparse property. To address the above problem, we introduce redundant spectrum to represent the spectral variation caused by ion (atom) substitution and develop a sparse redundant unmixing model by adding the redundant regularization into the classical sparse regression formulation. Based on the alternating direction method of multipliers, we develop a unified algorithm called sparse redundant unmixing to obtain the solution. Both simulation experiment and real data experiment demonstrate that the proposed method can effectively use the redundant spectrum to address the spectral variation problem caused by the ion (atom) substitution.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Toward SI Traceability of a Monte Carlo Radiative Transfer Model in the
           Visible Range
    • Authors: Priit Jaanson;Agnieszka Bialek;Claire Greenwell;Henrik Mäntynen;Jean-Luc Widlowski;Farshid Manoocheri;Antti Lassila;Nigel Fox;Erkki Ikonen;
      Pages: 1360 - 1373
      Abstract: A 3-D Monte Carlo (MC) ray-tracing radiative transfer model is tested for its ability to simulate the bidirectional reflectance factors (BRFs) of a grooved artificial target given SI-traceable measurements of the optical and topographic properties of the target’s surface. The optical properties of a grooved target and an identical flat target were measured with the goniospectrophotometer at the National Metrology Institute of U.K. (NPL) and are traceable to the NPL scales of radiance factor. The topographic measurements were performed with the coordinate measuring machine at the National Metrology Institute of Finland (MIKES), and are traceable to the realization of the meter. The BRFs of the flat target were used to parameterize analytical scattering functions for rough surfaces. Similarly, the topographic measurement results were used to construct a structural model of the grooved target. Each element within this structural model then had its optical properties defined by the parameterized scattering function before the 3-D MC model simulated the BRFs of the grooved target under well-defined illumination and viewing conditions. The measured and modeled BRFs agreed for 72% of the measured geometries in the plane of incidence within the measurement and modeling uncertainties. The relative root-mean-squared (RMSE) error was 0.19. In the plane orthogonal to the plane of incidence, the measured and modeled BRFs agreed for 45% of the measured geometries, and the relative RMSE between measured and modeled values was 0.65.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland
           Waters From Simulated MERIS and OLCI Data
    • Authors: Ge Liu;Stefan G. H. Simis;Lin Li;Qiao Wang;Yunmei Li;Kaishan Song;Heng Lyu;Zhubin Zheng;Kun Shi;
      Pages: 1374 - 1385
      Abstract: Existing remote-sensing algorithms to estimate the phycocyanin (PC) concentration in turbid inland waters have high associated uncertainties, especially at low PC concentrations in diverse phytoplankton communities. This paper provides the theoretical framework for a four-band semi-analytical algorithm (FBA_PC) which isolates PC absorption from second-order variability caused by yellow matter and other phytoplankton pigment absorption. The algorithm suits the band configuration of both the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3 Ocean and Land Color Instrument (OLCI). Calibration of the algorithm was based on absorption data from 12 inland water bodies in the USA, The Netherlands, and China, combined with measurements from laboratory-grown cultures, which demonstrated that the assumptions underlying FBA-PC are an improvement over existing three-band approaches. Validation of FBA_PC in seven inland water bodies in the USA, The Netherlands, and China showed good agreement of FBA_PC adjusted to the MERIS/OLCI band configuration with measured PC, with root-mean-square error =27.691 $text {mg} cdot text {m}^{-3}$ , mean absolute percentage error = 172.863%, and coefficient of determination ( $R^{2}) = 0.730$ . FBA_PC outperformed previously proposed PC algorithms that can be applied to MERIS or OLCI data, and is expected to be more robust when applied to a wider range of water bodies.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Autofocusing Technique Based on Generalized Multilayer Stolt Migration
    • Authors: Haewon Jung;Kangwook Kim;
      Pages: 1386 - 1393
      Abstract: A method is proposed for estimation of geometric information (GI) for obliquely layered geometry. The GI is estimated in the process of autofocusing (AF) of a ground-penetrating radar image. A novel AF technique is proposed based on the generalized multilayer Stolt migration algorithm. In the algorithm, the position and angle of the layer boundaries are determined using a Hough transform. At each layer, the AF metric is iteratively evaluated to estimate the relative permittivity (RP) of the layer. The performances of four AF metrics are compared, and an RP determination algorithm is suggested to reduce the number of AF metric evaluations. The proposed algorithm is validated with numerical and experimental data.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Soil Moisture Retrieval From SMAP: A Validation and Error Analysis Study
           Using Ground-Based Observations Over the Little Washita Watershed
    • Authors: Quan Chen;Jiangyuan Zeng;Chenyang Cui;Zhen Li;Kun-Shan Chen;Xiaojing Bai;Jia Xu;
      Pages: 1394 - 1408
      Abstract: The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and $0.041~text {m}^{3}cdot ~text {m}^{-3}$ , respectively, which generally meet their mission requirement of $0.04~text {m}^{3}cdot ~text {m}^{-3}$ . In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of $0.06~text {m}^{3}cdot ~text {m}^{-3}$ . The new enhanced passive product shows the lowest ubRMSE value of $0.026 ~text {m}^{3}cdot ~text {m}^{-3}$ though it underestimates in situ measurements with a bias of $0.059 ~-text {m}^{3}cdot ~text {m}^{-3}$ , revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Multifeature Hyperspectral Image Classification With Local and Nonlocal
           Spatial Information via Markov Random Field in Semantic Space
    • Authors: Xiangrong Zhang;Zeyu Gao;Licheng Jiao;Huiyu Zhou;
      Pages: 1409 - 1424
      Abstract: Hyperspectral images (HSIs) provide invaluable information in both spectral and spatial domains for image classification tasks. In this paper, we use semantic representation as a middle-level feature to describe image pixels’ characteristics. Deriving effective semantic representation is critical for achieving good classification performance. Since different image descriptors depict characteristics from different perspectives, combining multiple features in the same semantic space makes semantic representation more meaningful. First, a probabilistic support vector machine is used to generate semantic representation-based multifeatures. In order to derive better semantic representation, we introduce a new adaptive spatial regularizer that well exploits the local spatial information, while a nonlocal regularizer is also used to search for global patch-pair similarities in the whole image. We combine multiple features with local and nonlocal spatial constraints using an extended Markov random field model in the semantic space. Experimental results on three hyperspectral data sets show that the proposed method provides better performance than several state-of-the-art techniques in terms of region uniformity, overall accuracy, average accuracy, and Kappa statistics.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Multiple Kernel Learning for Remote Sensing Image Classification
    • Authors: Saeid Niazmardi;Begüm Demir;Lorenzo Bruzzone;Abdolreza Safari;Saeid Homayouni;
      Pages: 1425 - 1443
      Abstract: This paper presents multiple kernel learning (MKL) in the context of remote sensing (RS) image classification problems by illustrating main characteristics of different MKL algorithms and analyzing their properties in RS domain. A categorization of different MKL algorithms is initially introduced, and some promising MKL algorithms for each category are presented. In particular, MKL algorithms presented only in machine learning are introduced in RS. Then, the investigated MKL algorithms are theoretically compared in terms of their: 1) computational complexities; 2) accuracy with different qualities of kernels; and 3) accuracy with different numbers of kernels. After the theoretical comparison, experimental analyses are carried out to compare different MKL algorithms in terms of: 1) model selection and 2) feature fusion problems. On the basis of the theoretical and experimental analyses of MKL algorithms, some guidelines for a proper selection of the MKL algorithms are derived.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Compound-Plus-Noise Model for Improved Vessel Detection in Non-Gaussian
           SAR Imagery
    • Authors: Christoph H. Gierull;Ishuwa Sikaneta;
      Pages: 1444 - 1453
      Abstract: The commonly applied K-distribution to model the synthetic aperture radar image amplitude of the heterogeneous (non-Gaussian) sea surface as the basis for vessel detection has shown deficiencies in practical cases, particularly for space-based systems. Due to a deviation between the K-probability density function and measured histograms in the tails, even the inclusion of thermal noise is oftentimes not sufficient to cover the range of environments that are expected. As a consequence, virtually all detectors try to reduce the large number of obtained false detections by relying on rather heuristic postprocessing steps. Consequently, they forfeit the crucial property of a constant false alarm rate. This paper proposes a novel statistical sea clutter model that describes the data more accurately, especially in challenging environments and thermal-noise limited cases. This new model stands out through its numerical simplicity, permitting efficient parameter adaptation thereby enhancing robustness and reducing computational complexity. Accordingly, the presented sea data model has the potential to replace the widely adopted K-distribution as model of choice for future operational applications.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Nonambiguous SAR Image Formation of Maritime Targets Using Weighted Sparse
    • Authors: Gang Xu;Xiang-Gen Xia;Wei Hong;
      Pages: 1454 - 1465
      Abstract: For a single-channel synthetic aperture radar (SAR), finite-pulse repetition frequency and nonideal antenna pattern cause azimuth ambiguities, i.e., ghosts in image domain. In this paper, a novel algorithm of locating processing weighted group lasso SAR image formation for maritime targets is proposed to effectively mitigate the ambiguities, which can work on a single-look complex SAR image. In the scheme, the ambiguous signal model using the conventional SAR focusing processor is first explicitly derived, showing the analytical expression of SAR image formulation. The weighted sparse group lasso algorithm is then employed to group-sparsely reconstruct the subimages of nonambiguous and ambiguous Doppler components. In particular, we introduce adaptively weighted sparsity constraint, obtained from a priori azimuth antenna pattern, and clutter clustering during sparse imaging. It should be emphasized that the proposed algorithm can effectively improve the azimuth resolution by coherently integrating the ambiguous signal components, which greatly helps the target detection and recognition in maritime surveillance. Finally, experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Correction of Sensor Saturation Effects in MODIS Oceanic Particulate
           Inorganic Carbon
    • Authors: Peter E. Land;Jamie D. Shutler;Timothy J. Smyth;
      Pages: 1466 - 1474
      Abstract: The highly reflective nature of high particulate inorganic carbon (PIC) from calcifying plankton, such as surface blooms of Emiliana huxleyi in the latter stages of their life cycle, can cause the saturation of the Moderate Resolution Imaging Spectrometer (MODIS) visible spectrum ocean color bands. This saturation results in errors in the standard MODIS oceanic PIC product, resulting in the highest PIC levels being represented as cloud-like gaps (missing data) in daily level 2 data, and as either gaps or erroneously low PIC values in temporally averaged data (e.g., 8-day level 3 data). A method is described to correct this error and to reconstruct the missing data in the ocean color band data by regressing the 1-km spatial resolution ocean color bands against MODIS higher resolution (500 m spatial resolution) bands with lower sensitivities. The method is applied to all North Atlantic MODIS data from 2002 to 2014. This shows the effect on mean PIC concentration over the whole North Atlantic to be less than 1% annually and 2% monthly, but with more significant regional effects, exceeding 10% in peak months in some coastal shelf regions. Effects are highly localized and tend to annually reoccur in similar geographical locations. Ignoring these missing data within intense blooms is likely to result in an underestimation of the influence that coccolithophores, and their changing distributions, are having on the North Atlantic carbon cycle. We see no evidence in this 12-year time series of a temporal poleward movement of these intense bloom events.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Time Delay and Interface Roughness Estimation Using Modified ESPRIT With
           Interpolated Spatial Smoothing Technique
    • Authors: Meng Sun;Cédric Le Bastard;Yide Wang;Nicolas Pinel;Jingjing Pan;Vincent Baltazart;Jean-Michel Simonin;Xavier Dérobert;
      Pages: 1475 - 1484
      Abstract: In civil engineering, ground penetrating radar is a common technique for evaluating the structure and quality of road pavement. This paper focuses on the estimation of the time delay and interface roughness of civil engineering structure, like pavements. The influence of interface roughness is taken into account in the signal model. A modified estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm combined with an interpolated spatial smoothing technique is proposed. It allows us to jointly and efficiently estimate the time delay and interface roughness by ultrawideband radar (the upper frequency up to 8–10 GHz) with low computational complexity. The proposed algorithm is tested on both numerical and experimental data. Simulation and experimental results show the good performance of the proposed algorithm.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • 500–2000-MHz Brightness Temperature Spectra of the Northwestern
           Greenland Ice Sheet
    • Authors: Kenneth C. Jezek;Joel T. Johnson;Shurun Tan;Leung Tsang;Mark J. Andrews;Marco Brogioni;Giovanni Macelloni;Michael Durand;Chi-Chih Chen;Domenic J. Belgiovane;Yuna Duan;Caglar Yardim;Hongkun Li;Alexandra Bringer;Vladimir Leuski;Mustafa Aksoy;
      Pages: 1485 - 1496
      Abstract: An ultra-wideband radiometer has been developed to measure subsurface properties of the cryosphere including ice sheets and sea ice. The radiometer measures brightness temperature spectra from 0.5 to 2 GHz using 12 channels, each of which measures scene brightness temperatures over an ~88-MHz bandwidth resolved into 0.24-MHz intervals. The instrument was flown over northwestern Greenland in September 2016 and acquired the first, wideband, low-frequency brightness temperature spectra over the ice sheet and coastal region. The results reveal strong spatial and spectral variations that correlate well with the physical properties of the surface encountered along the flight path, which started over ocean, then passed the rock near the coast, and then up onto the ablation, wet, percolation, and dry snow zones of the interior ice sheet. In particular, strong spectral responses in percolation and dry snow zones are observed and plausibly explained by varying the distribution of horizontal density layers and isolated icy bodies in the upper portion of the firn. The success of the airborne deployment of the instrument and subsequent implementation of algorithms to limit radio frequency interference in unprotected bands is motivating continued airborne investigations as well as stimulating research into the feasibility of a spaceborne instrument.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Measuring Multiresolution Surface Roughness Using V-System
    • Authors: Wei Cao;Zhanchuan Cai;Ben Ye;
      Pages: 1497 - 1506
      Abstract: Surface roughness is a land-surface parameter that is widely used in terrain analysis. Some typical roughness details, which have important effects on surface analysis, fail to be characterized on previous roughness maps. The objective of this paper is to provide a more accurate small-to-large scale roughness overview. The new roughness method is designed based on a complete orthogonal system called the V-system. The V-system roughness utilizes the special functions to detect and extract the roughness characteristics from high-resolution digital elevation models (DEMs). In this paper, Lunar Orbiter Laser Altimeter-derived DEMs are used as the source data for the roughness calculation. Compared with the global root-mean-square slope and Fourier-based roughness maps, the V-system roughness maps show that more typical roughness details have been added to clearly indicate the small roughness variations on the large map. Furthermore, the reliability and practicability of V-system roughness are demonstrated based on the multiresolution DEMs. As an example, the statistical parameters of the roughness characteristics in the lunar Maria and highlands identify the fact that the highlands are rougher at all scales than the Maria. And this difference corresponds to the basic roughness property.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Intercalibration of Polar-Orbiting Spectral Radiometers Without
           Simultaneous Observations
    • Authors: Terhikki Manninen;Aku Riihelä;Andrew Heidinger;Crystal Schaaf;Alessio Lattanzio;Jeffrey Key;
      Pages: 1507 - 1519
      Abstract: A new intercalibration method for two polar-orbiting satellite instruments or two instrument constellations’ Fundamental Climate Data Records (FCDRs) is presented. It is based on statistical fitting of reflectance data from the two instruments covering the same area during the same period, but not simultaneously. A Deming regression with iterative weights is used. The accuracy of the intercalibration method itself was better than 0.5% for the Moderate Resolution Imaging Spectroradiometer (MODIS) versus MODIS and Advanced Very High Resolution Radiometer (AVHRR) versus AVHRR test data sets. The intercalibration of an AVHRR FCDR generated by NOAA versus a combined MODIS Terra and Aqua data set of red and near-infrared (NIR) channels was carried out and showed a difference in the reflectance values of about 2% (red) and 6% (NIR). The presented intercalibration method can be used for checking the calibration of two instruments or FCDRs in all viewing angles used separately.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Damage-Mapping Algorithm Based on Coherence Model Using Multitemporal
           Polarimetric–Interferometric SAR Data
    • Authors: Jungkyo Jung;Sang-Ho Yun;Duk-jin Kim;Marco Lavalle;
      Pages: 1520 - 1532
      Abstract: This paper presents a new damage-mapping algorithm based on coherence images estimated from multitemporal polarimetric–interferometric synthetic aperture radar (SAR) data. The interferometric coherence has been restricted in the conventional damage-mapping approaches because the decorrelation sources are too complicated to interpret accurately and temporal decorrelation effects caused by slowly occurring natural changes and disaster events are often coupled together. To overcome these limitations, we formulate a coherence model that accounts for temporal decorrelation in two simplified layers, ground and volume layers, for long-temporal repeat-pass scenarios with zero spatial baseline. The model parameters include: 1) ground-to-volume ratio, a factor to determine the relative scattering contribution of ground and volume layers; 2) temporally correlated change, which captures the exponentially decaying behavior of coherence with time; and 3) temporally uncorrelated change, which is associated with random temporal changes. We estimate the model parameters in three steps: coherence optimization, interferometric pair-invariant parameter estimation, and interferometric pair-variant parameter estimation. To isolate the effects of disaster events from background natural changes, we calculate the probability density functions of historical change pixel by pixel and produce a probability map of damage. We tested the algorithm with uninhabited aerial vehicle data acquired from 2009 to 2015 for mapping the area damaged by the 2015 Lake Fire in California. Based on performance evaluation using receiver operating characteristic curves for optimized coherences and averaged probability maps, the proposed method reduced the false alarm from 0.25 to 0.07 when the probability of detection was 0.85 compared to coherence products alone.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Two-Level Block Matching Pursuit for Polarimetric Through-Wall Radar
    • Authors: Xueqian Wang;Gang Li;Yu Liu;Moeness G. Amin;
      Pages: 1533 - 1545
      Abstract: In this paper, we propose a two-level block matching pursuit (TLBMP) algorithm based on a probabilistic graph model for polarimetric through-wall radar imaging (TWRI). In typical L-band to X-band TWRI, indoor targets assume a spatial extent and occupy clustered pixels. When polarimetric sensing is used to obtain independent observations, radar images of clustered targets can be enhanced within the joint sparsity framework. Toward this objective, TLBMP is devised to exploit both the clustered property and the joint sparsity pattern of multiple polarimetric through-wall radar images. Simulations and experimental results based on polarimetric through-wall radar data demonstrate that compared to commonly used algorithms for solving the same underlying problem, TLBMP provides more informative imaging with higher target-to-clutter ratio.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Global ECMWF Analysis Data for Estimating the Water Vapor Content Between
           Two LEO Satellites Through NDSA Measurements
    • Authors: Luca Facheris;Fabrizio Cuccoli;
      Pages: 1546 - 1554
      Abstract: The normalized differential spectral attenuation (NDSA) approach was proposed years ago as an effective way to estimate the integrated water vapor (IWV) along a tropospheric propagation path between two low Earth orbit satellites. Two applications are possible: the retrieval of vertical profiles of WV if the sense of rotation is opposite and the retrieval of 2-D fields of WV over vertical tropospheric sections if the sense is the same. The method relies on the measurement of the so-called spectral sensitivity $S$ at given frequencies, and on IWV-S relationships that convert $S$ into an estimate of IWV along the radio link where $S$ is measured. In this paper, we recompute the IWV-S relationships using synthetic atmospheres generated by means of European Centre for Medium-Range Weather Forecasts (ECMWF) analysis data instead of radiosonde profiles as done by ourselves in the past. Thanks to the uniform spatial distribution of the ECMWF data on a global Earth scale, we were able to validate the IWV-S relationships in the Ku/K band previously found through synthetic atmospheres generated by means of the aforementioned irregularly spaced radiosonde data, and to define the IWV-S relationships at 179 and 181 GHz that are exploitable in the upper troposphere. Since the ECMWF data also include information about the liquid water (LW) content, we then show that an additional $S$ channel at 32 GHz can be exploited to detect and correct the bias induced by LW on IWV estimates made by applying the NDSA in the Ku/K band.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization
    • Authors: Jeong-Won Park;Anton A. Korosov;Mohamed Babiker;Stein Sandven;Joong-Sun Won;
      Pages: 1555 - 1565
      Abstract: The intensity of a Sentinel-1 Terrain Observation with Progressive Scans synthetic aperture radar image is disturbed by additive thermal noise, particularly in the cross-polarization channel. Although the European Space Agency provides calibrated noise vectors for noise power subtraction, residual noise contributions are significant when considering the relatively narrow backscattering distribution of the cross-polarization channel. In this paper, we investigate the characteristics of noise and propose an efficient method for noise reduction based on a three-step correction process comprised of azimuth descalloping, noise scaling and interswath power balancing, and local residual noise power compensation. The core idea is to find the optimal correction coefficients resulting in the most noise-uncorrelated gentle backscatter profile over a homogeneous region and to combine them with the scalloping gain for a reconstruction of the complete 2-D noise field. Denoising is accomplished by subtracting the reconstructed noise field from the original image. The performance improvement in some applications by adopting the denoising procedure shows the effectiveness of the proposed method.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Detecting Changes Between Optical Images of Different Spatial and Spectral
           Resolutions: A Fusion-Based Approach
    • Authors: Vinicius Ferraris;Nicolas Dobigeon;Qi Wei;Marie Chabert;
      Pages: 1566 - 1578
      Abstract: Change detection (CD) is one of the most challenging issues when analyzing remotely sensed images. Comparing several multidate images acquired through the same kind of sensor is the most common scenario. Conversely, designing robust, flexible, and scalable algorithms for CD becomes even more challenging when the images have been acquired by two different kinds of sensors. This situation arises in the case of emergency under critical constraints. This paper presents, to the best of our knowledge, the first strategy to deal with optical images characterized by dissimilar spatial and spectral resolutions. Typical considered scenarios include CD between panchromatic, multispectral, and hyperspectral images. The proposed strategy consists of a three-step procedure: 1) inferring a high spatial and spectral resolution image by fusion of the two observed images characterized one by a low spatial resolution and the other by a low spectral resolution; 2) predicting two images with, respectively, the same spatial and spectral resolutions as the observed images by the degradation of the fused one; and 3) implementing a decision rule to each pair of observed and predicted images characterized by the same spatial and spectral resolutions to identify changes. To quantitatively assess the performance of the method, an experimental protocol is specifically designed, relying on synthetic yet physically plausible change rules applied to real images. The accuracy of the proposed framework is finally illustrated on real images.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Recent Advances on Spectral–Spatial Hyperspectral Image Classification:
           An Overview and New Guidelines
    • Authors: Lin He;Jun Li;Chenying Liu;Shutao Li;
      Pages: 1579 - 1597
      Abstract: Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the last four decades from being a sparse research tool into a commodity product available to a broad user community. Specially, in the last 10 years, a large number of new techniques able to take into account the special properties of hyperspectral data have been introduced for hyperspectral data processing, where hyperspectral image classification, as one of the most active topics, has drawn massive attentions. Spectral–spatial hyperspectral image classification can achieve better classification performance than its pixel-wise counterpart, since the former utilizes not only the information of spectral signature but also that from spatial domain. In this paper, we provide a comprehensive overview on the methods belonging to the category of spectral–spatial classification in a relatively unified context. First, we develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral–spatial methods. Based on such, the categorizations of single-dependency, bilayer-dependency, and multiple-dependency systems are further introduced. Second, we categorize the performings of existing spectral–spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e., preprocessing-based, integrated, postprocessing-based, and hybrid classifications. Then, typical methodologies are outlined. Finally, several representative spectral–spatial classification methods are applied on real-world hyperspectral data in our experiments.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • High-Resolution Mapping of Near-Field Deformation With Airborne Earth
           Observation Data, a Comparison Study
    • Authors: Nima Ekhtari;Craig Glennie;
      Pages: 1598 - 1614
      Abstract: We present an investigation into different approaches for high-resolution mapping of near-field surface displacement for strike-slip earthquakes. Airborne laser scanning (ALS) and optical imagery are two common sources of earth observation data available to geoscientists for earthquake documentation and studies. Optical image correlation and point cloud differencing techniques are among the most widely used methods for retrieving displacement signals in the near field. We compare the performances of these techniques for estimating near-field deformation using pre and postevent high-resolution ALS and airborne imagery of the August 24, 2014 Mw 6.0 Napa, California earthquake. Estimates of deformation agree with field observations within a decimeter, at the expected accuracy level of the data. We show that the correlation of intensity images from ALS data can unveil the near-field deformation successfully and outperforms optical image correlation in vegetated areas as well as in the absence of geodetic markers (man-made structures). Furthermore, we illustrate that the point clouds generated with structure from motion perform comparably to ALS point clouds for retrieving the displacement signal in unvegetated areas. Overall, we conclude that ALS data are generally better than imagery for estimating near-field deformation regardless of the estimation methodology and that the iterative closest point algorithm was more effective at recovering the displacement signal.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Waveform-Preserving Processing Flow of Multichannel Seismic Reflection
           Data for Adjoint-State Full-Waveform Inversion of Ocean Thermohaline
    • Authors: Daniel Dagnino;Valentí Sallarès;César R. Ranero;
      Pages: 1615 - 1625
      Abstract: This paper presents a specific data processing flow to be applied to marine multichannel seismic reflection data collected by a streamer in order to use them to perform prestack adjoint waveform inversion of ocean’s thermohaline properties. The overall goal is to increase the signal-to-noise ratio (SNR) of the weak reflections generated at the small impedance contrasts within the water layer while preserving the direct wave. The processing flow focuses on increasing the SNR of the shot gather records by forcing noise amplitudes to fall inside a range of physical plausible values for water layer reflections. This processing step is applied in two independent branches of the workflow; one dealing with the water layer reflections and the second with the direct wave, which are separated by applying a singular value decomposition. To test the performance of the processing flow, we combine actual noise field recordings with a synthetic seismic data set. We apply the proposed data processing flow to quantify differences between noise-free and processed record sections, and we then compare with the results obtained by applying a Butterworth filter (BF). For offsets smaller than 1500 m, the BF processing produces a signal with SNR < 0.1; the proposed workflow allows to retrieve the seismic signal with 0.1 < SNR < 2.4. For offsets larger than 1500 m, the BF processing allows obtaining the SNR up to 1.4, while the proposed workflow increases the SNR up to 5. We finally demonstrate that the processed data can be used to perform waveform inversion with an accuracy of ~0.02 m/s.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Parisar: Patch-Based Estimation and Regularized Inversion for
           Multibaseline SAR Interferometry
    • Authors: Giampaolo Ferraioli;Charles-Alban Deledalle;Loic Denis;Florence Tupin;
      Pages: 1626 - 1636
      Abstract: Reconstruction of elevation maps from a collection of synthetic aperture radar (SAR) images obtained in interferometric configuration is a challenging task. Reconstruction methods must overcome two difficulties: the strong interferometric noise that contaminates the data and the $2pi $ phase ambiguities. Interferometric noise requires some form of smoothing among pixels of identical height. Phase ambiguities can be solved, up to a point, by combining linkage to the neighbors and a global optimization strategy to prevent from being trapped in local minima. This paper introduces a reconstruction method, Parisar, that achieves both a resolution-preserving denoising and a robust phase unwrapping (PhU) by combining nonlocal denoising methods based on patch similarities and total-variation regularization. The optimization algorithm, based on graph cuts, identifies the global optimum. Combining patch-based speckle reduction methods and regularization-based PhU requires solving several issues: 1) computational complexity, the inclusion of nonlocal neighborhoods strongly increasing the number of terms involved during the regularization, and 2) adaptation to varying neighborhoods, patch comparison leading to large neighborhoods in homogeneous regions and much sparser neighborhoods in some geometrical structures. Parisar solves both issues. We compare Parisar with other reconstruction methods both on numerical simulations and satellite images and show a qualitative and quantitative improvement over state-of-the-art reconstruction methods for multibaseline SAR interferometry.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Lake Icepack and Dry Snowpack Thickness Measurement Using Wideband
           Autocorrelation Radiometry
    • Authors: Seyedmohammad Mousavi;Roger D. De Roo;Kamal Sarabandi;Anthony W. England;Sing Yee Emily Wong;Hamid Nejati;
      Pages: 1637 - 1651
      Abstract: A novel microwave radiometric technique, wideband autocorrelation radiometry (WiBAR), is introduced. The radiometer offers a direct method to remotely measure the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces, such as a dry snowpack and a freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent; thus, this technique provides a direct measurement of depth. It is also a low-power sensing method, since there is no transmitter. We present a simple geophysical forward model for the multipath interference phenomenon and derive the system requirements needed to design a WiBAR instrument. An X-band instrument fabricated from commercial-off-the-shelf (COTS) components measured the thickness of the freshwater lake ice at the University of Michigan Biological Station. Ice thickness retrieval is demonstrated from nadir to 59°. The WiBAR was able to directly measure the lake icepack thickness of about 36 cm with an accuracy of 2 cm over this range of incidence angles.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Convex Optimization-Based Coupled Nonnegative Matrix Factorization
           Algorithm for Hyperspectral and Multispectral Data Fusion
    • Authors: Chia-Hsiang Lin;Fei Ma;Chong-Yung Chi;Chih-Hsiang Hsieh;
      Pages: 1652 - 1667
      Abstract: Fusing a low-spatial-resolution hyperspectral data with a high-spatial-resolution (HSR) multispectral data has been recognized as an economical approach for obtaining HSR hyperspectral data, which is important to accurate identification and classification of the underlying materials. A natural and promising fusion criterion, called coupled nonnegative matrix factorization (CNMF), has been reported that can yield high-quality fused data. However, the CNMF criterion amounts to an ill-posed inverse problem, and hence, advisable regularization can be considered for further upgrading its fusion performance. Besides the commonly used sparsity-promoting regularization, we also incorporate the well-known sum-of-squared-distances regularizer, which serves as a convex surrogate of the volume of the simplex of materials’ spectral signature vectors (i.e., endmembers), into the CNMF criterion, thereby leading to a convex formulation of the fusion problem. Then, thanks to the biconvexity of the problem nature, we decouple it into two convex subproblems, which are then, respectively, solved by two carefully designed alternating direction method of multipliers (ADMM) algorithms. Closed-form expressions for all the ADMM iterates are derived via convex optimization theories (e.g., Karush–Kuhn–Tucker conditions), and furthermore, some matrix structures are employed to obtain alternative expressions with much lower computational complexities, thus suitable for practical applications. Some experimental results are provided to demonstrate the superior fusion performance of the proposed algorithm over state-of-the-art methods.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Atmospheric Phase Screen in GEO-SAR: Estimation and Compensation
    • Authors: Andrea Monti Guarnieri;Antonio Leanza;Andrea Recchia;Stefano Tebaldini;Giovanna Venuti;
      Pages: 1668 - 1679
      Abstract: We study the impact of atmospheric turbulence, specifically the wet tropospheric delay, in that synthetic aperture radar (SAR) with very long integration time, from minutes to hours, and wide swaths, such as the geosynchronous or geostationary SAR. In such systems, the atmospheric phase screen (APS) cannot be assumed frozen in time as for Low Earth Orbit or airborne SARs nor constant in space as for the ground-based SAR. The impact of space–time turbulence on SAR focusing is quantitatively assessed, and a novel focusing method that integrates APS estimation and compensation is proposed. Performances are evaluated as a function of SAR parameters, mainly the wavelength, based on a parametric model of the APS variogram, and results achieved by a simulating realistic scenario are shown.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Low-Rank Decomposition and Total Variation Regularization of Hyperspectral
           Video Sequences
    • Authors: Yang Xu;Zebin Wu;Jocelyn Chanussot;Mauro Dalla Mura;Andrea L. Bertozzi;Zhihui Wei;
      Pages: 1680 - 1694
      Abstract: Hyperspectral video sequences (HVSs) are well suited for gas plume detection (GPD). The high spectral resolution allows the detection of chemical clouds even when they are optically thin. Processing this new type of video sequences is challenging and requires advanced image and video analysis algorithms. In this paper, we propose a novel method for GPD recorded in HVSs. Based on the assumption that the background is stationary and the gas plume is moving, the proposed method separates the background from the gas plume via a low-rank and sparse decomposition. Furthermore, taking into consideration that the gas plume is continuous in both spatial and temporal dimensions, we include total variation regularization in the constrained minimization problem, which we solve using the augmented Lagrangian multiplier method. After applying the above process to each extracted feature, a novel fusion strategy is proposed to combine the information into a final detection result. Experimental results using real data sets indicate that the proposed method achieves very promising GPD performance.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Characterization of Unstable Blinking Pixels in the AisaOWL Thermal
           Hyperspectral Imager
    • Authors: Laura Harris;Gary M. Llewellyn;Hannu Holma;Mark A. Warren;Daniel Clewley;
      Pages: 1695 - 1703
      Abstract: The AisaOWL thermal hyperspectral instrument, manufactured by Specim, is a relatively new push-broom sensor well suited to airborne environmental surveys. The sensor covers the 7.6– $12.6~mu text{m}$ part of the long-wave infrared region with 102 continuous bands, and is capable of imaging in low-light conditions. The detector array is a mercury cadmium telluride (MCT) semiconductor, which has an inherent randomly varying dark current for random pixels. This manifests in the raw data as a pixel switching between different intensity levels. These pixels are termed “blinkers” by the manufacturer. For each data acquisition, the pixels need to be tested for blinking behavior as different pixels are affected during each acquisition. However, little is known about the number of blink events, the duration of frames, or the optimal length of data acquisition. This paper presents the characterization of the blinking nature of pixels in the MCT detector array to provide guidance on data acquisition and processing. This paper finds that blinking behavior is not completely random, with some pixels more prone to blinking behavior than others. Most blinking pixels have only a few short blinks; therefore, there is still a considerable amount of good data in a blinking pixel.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Hybrid Sparsity and Distance-Based Discrimination Detector for
           Hyperspectral Images
    • Authors: Xiaoqiang Lu;Wuxia Zhang;Xuelong Li;
      Pages: 1704 - 1717
      Abstract: Hyperspectral target detection is an approach which tries to locate targets in a hyperspectral image on the condition of given targets spectrum. Many classical target detectors are based on the linear mixing model (LMM) and sparsity model. The LMM has a poor performance in dealing with the spectral variability. Therefore, more studies focus on the sparsity-based detectors, most of which are based on residual reconstruction. Owing to the fact that the impure dictionary for the test pixel weakens the detection performance and the discrimination ability of residual function has direct influence on the detecting accuracy, the dictionary purity and discriminative residual function are two most important factors affecting the accuracy of sparsity-based target detectors. In order to obtain more purified dictionary and discriminative residual function, this paper proposes a novel sparsity-based detector named the hybrid sparsity and distance-based discrimination (HSDD) detector for target detection in hyperspectral imagery. The residual function is constrained by the discrimination information during the dictionary construction, which enhances the dictionary purification. Only background samples are used to construct the dictionary because it is easier to remove the target pixel than to select it on the condition that majority of pixels are the background pixels. Hence, a purification process is applied for background training samples in order to construct an effective competition between the residual term and discriminative term. Extensive experimental results with four hyperspectral data sets demonstrate that the proposed HSDD algorithm has a better performance than the state-of-the-art algorithms.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Joint Gaussian Processes for Biophysical Parameter Retrieval
    • Authors: Daniel Heestermans Svendsen;Luca Martino;Manuel Campos-Taberner;Francisco Javier García-Haro;Gustau Camps-Valls;
      Pages: 1718 - 1727
      Abstract: Solving inverse problems is central in geosciences and remote sensing. The radiative transfer models (RTMs) represent mathematically the physical laws that rule the phenomena in remote sensing applications (forward models). The numerical inversion of the RTM equations is a challenging and computationally demanding problem. For this reason, often the application of a simpler statistical regression is preferred. In general, the regression models predict the biophysical parameter of interest from the corresponding received radiance, learning a mapping from in situ data. However, this approach does not employ the physical information encoded in the RTMs. An alternative strategy, which attempts to include the physical knowledge, consists in learning a regression model trained using simulated data by an RTM code. In this paper, we introduce a nonlinear nonparametric regression model that combines the benefits of the two aforementioned approaches. The inversion is performed considering jointly both real observations and RTM-simulated data. The proposed joint Gaussian process (JGP) provides a solid framework for exploiting the regularities between the two types of data, in order to perform inverse modeling. The JGP automatically detects the relative quality of the simulated and real data, and combines them properly. This occurs by learning an additional hyperparameter with respect to a standard Gaussian process model, so that the novel scheme is at the same time simple and robust, i.e., capable of adapting to different scenarios. The advantages of the JGP method compared with benchmark strategies are shown considering synthetic and real data in different experiments. Specifically, we consider leaf area index retrieval from Landsat data combined with simulated data generated by the PROSAIL model.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Improvements in the On-Orbit Response Versus Scan Angle Characterization
           of the Aqua MODIS Reflective Solar Bands
    • Authors: Amit Angal;Xiaoxiong Xiong;Aisheng Wu;Xu Geng;Hongda Chen;
      Pages: 1728 - 1738
      Abstract: The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) has been chosen by the Global Space-based Inter-Calibration System operational community as the reference sensor in cross-sensor calibration. A number of geostationary orbit and low-earth orbit sensors use the 0.64- $mu text{m}$ band from Aqua MODIS as a calibration reference. After over 15 years on-orbit, the performance characteristics of the MODIS instrument have changed, with effects evident at short wavelengths. MODIS employs a reflectance-based calibration using the solar diffuser measurements with the monthly lunar observations facilitating a response versus scan angle characterization on-orbit. As the instrument continues to operate beyond its design lifetime of 6 years, the on-board calibrators alone are insufficient to accurately characterize the instrument’s response at all scan angles. This results in a long-term reflectance drift, particularly observed in the 0.64- and 0.85- $mu text{m}$ bands, while observing the temporally invariant desert sites. Long-term reflectance drifts of up to 2% and 3% are observed at the beginning of scan for the 0.64- and 0.85- $mu text{m}$ bands, respectively. An approach using earth-view response to supplement the on-board calibrator measurements has been shown to overcome these inadequacies and is now implemented for the 0.64-, 0.85-, 0.46-, and 0.55- $mu text{m}$ land bands of Aqua MODIS. This paper presents the details related to the algorithm implementation and an independent evaluation using the Dome Concordia site and deep-convective clouds. This approach has been reviewed, tested, and approved by the MODIS -cience team and has been implemented in the forward production of the MODIS L1B Collection 6 starting July 9, 2016. This enhanced approach has also been adopted in the MODIS L1B Collection 6.1 reprocess for the entire mission to facilitate an improved quality of downstream science products. The results with the enhanced approach reduce the reflectance drifts to within 0.5% for most cases.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Imaging Spectroscopy BRDF Correction for Mapping Louisiana’s
           Coastal Ecosystems
    • Authors: Daniel J. Jensen;Marc Simard;Kyle C. Cavanaugh;David R. Thompson;
      Pages: 1739 - 1748
      Abstract: This paper presents the adaptive reflectance geometric correction (ARGC), a bidirectional reflectance distribution function (BRDF) correction algorithm to address intensity gradients across remotely sensed images. The ARGC is developed and tested on data from the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) collected over Louisiana’s Atchafalaya River Delta, an area of complex wetland vegetation and waterbodies suited to AVIRIS-NG’s fine spatial and spectral resolutions. Changing view and solar geometry, in conjunction with surfaces’ anisotropic properties, impact a scene’s observed reflectance. As traditional BRDF corrections may not be appropriate for wetland environments that have distinctive vegetation and hydrologic structures, more flexible functional corrections are shown to improve results. We compared two existing methods and the ARGC. The first method fits a quadratic function over image column averages, and the second is based on the inversion of the Ross Thick and Li Sparse kernels. Building upon the principles of these methods, the ARGC uses a multiple regression-based BRDF correction whereby the image’s solar and view geometric descriptors form the independent variables. Each BRDF correction method was applied to the set of six partially overlapping AVIRIS-NG scenes. Assuming the actual surface reflectance of a given land cover type is independent of geometry, we used adjacent images’ overlapping regions to quantitatively assess each correction method’s efficacy. The ARGC produced the lowest overall root-mean-square difference and the lowest overlap mean absolute difference across the vast majority of bands. The ARGC is proposed as a practical new BRDF correction option for investigators using AVIRIS-NG data.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Spectral Inter-Band Discrimination Capacity of Hyperspectral Imagery
    • Authors: Chein-I Chang;
      Pages: 1749 - 1766
      Abstract: This paper introduces a new concept of band capacity (BC) of a hyperspectral image and further develops a theory for BC. Its idea is derived from information theory where a band channel can be constructed from a hyperspectral image with both its channel input space and channel output space specified by its full band set and the channel transition probabilities between the input and output spaces characterized by between-band discrimination. In particular, a transition probability from a spectral band in the band channel input space to a spectral band in the band channel output space is calculated by their spectral discriminatory power/probability. By virtue of such a formulated band channel, its maximal mutual information can be defined as BC of a hyperspectral image to represent spectral discriminatory power per band measured by bits. Interestingly, BC provides a key to bridging the concept of virtual dimensionality defined as the number of spectrally distinct signatures and effective band dimensionality to be used to discriminate these spectrally distinct signatures one from another. Accordingly, an immediate application of BC is to determine the number of bands to be selected, $n_{mathrm {BS}}$ . Another application is band selection with the output space specified by a selected $n_{mathrm {BS}}$ -band subset. In this case, when BC is close to one, the selected band set tends to be optimal.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Dielectric and Radio-Emission Properties of Oil-Polluted Soils
    • Authors: Andrei N. Romanov;
      Pages: 1767 - 1773
      Abstract: The results of laboratory measurements of dielectric properties of oil-polluted sandy, clayey, and saline soils at variations of volume humidity and temperature in the range between 260 and 300 K at 1.41-GHz frequency are given. It is shown that dielectric and radio-emission properties of dry sand and oil within the error are temperature independent in the range from 260 to 315 K. The influence of oil on the microwave emission of soils is observed when volume humidity exceeds the maximum amount of bound water. The occurrence of oil additives in the soil leads to variations in dielectric and radio-emission properties of soils due to the change of the phase composition of soil moisture. Oil pollution of saline soils causes a dramatic change in their dielectric and radio-emission properties. Dielectric properties of the saline soils with the addition of oil in the range of positive temperatures depend on the ratio between humidity and soil salinity.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • GMTI and Parameter Estimation for MIMO SAR System via Fast Interferometry
           RPCA Method
    • Authors: Yan Huang;Guisheng Liao;Jingwei Xu;Jie Li;Dong Yang;
      Pages: 1774 - 1787
      Abstract: Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention because of its extra degrees of freedom for high resolution and wide swath compared with the traditional multichannel SAR system. But how to extract the matched signal without the unmatched interferences is the foremost task for MIMO SAR system. In this paper, by using the orthogonal frequency division multiplexing chirp signals as the transmitted signals, it is demonstrated that the robust principal component analysis (RPCA) method can be successfully employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. It is because the unmatched interference is proven to have low-rank property and noise-level magnitude, which can be separated apart from the matched signal with the RPCA method. However, the traditional RPCA methods may be restricted by the high computational burden due to the complex decompositions and multiple iterations. Hence, a fast interferometry RPCA method is proposed specially for GMTI mode, which takes full advantage of the characteristics of along-track interferometry SAR system. It can improve the probability of detection under low signal-to-clutter-and-noise ratio. Additionally, it will dramatically shorten the computational time. Furthermore, the proposed method can also estimate the radial velocities of the moving targets simultaneously. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • A Variational Pan-Sharpening Method Based on Spatial Fractional-Order
           Geometry and Spectral–Spatial Low-Rank Priors
    • Authors: Pengfei Liu;Liang Xiao;Tao Li;
      Pages: 1788 - 1802
      Abstract: Pan-sharpening refers to the fusion of a low-resolution (LR) multispectral (MS) image and a high-resolution (HR) panchromatic (PAN) image to obtain an HR MS image (i.e., pan-sharpened MS image). From the point of view of variational complementary data fusion, it becomes an optimization problem with geometry and spectral preserving constraints. In this paper, a novel unified optimizing pan-sharpening model is proposed by integrating a data-generative fidelity term and a compound prior term, which incorporates both spatial fractional-order geometry and spectral–spatial low-rank priors. Specifically, the proposed model consists of three important ingredients: 1) data-generative fidelity term, which models the degradation relationship between the LR and HR MS images to enforce the geometry and spectral preserving constraints; 2) fractional-order total variation-based spatial fractional-order geometry prior term, which especially exploits the spatial fractional-order gradient feature consistence between the PAN and pan-sharpened MS images to transfer the spatial structure information of the PAN image into the pan-sharpened MS image; and 3) weighted nuclear norm-based spectral–spatial low-rank prior term, which exploits the nonlocal patches-based low-rank structural sparsity simultaneously in the pan-sharpened MS image and the LR MS image for further preserving image spatial structures and spectral information. Thus, the main novelty behind the proposed model is an optimizing mechanism by fully taking advantage of the spatial details and texture expressive power of the spatial fractional-order geometry prior as well as the spectral–spatial correlation preserving capacity of the low-rank prior. Finally, the proposed model can be implemented in an alternating direction method of multipliers framework, and thus- an efficient algorithm is presented. To verify the validity, the new proposed method is systematically compared with some state-of-the-art techniques using the Pleiades, GeoEye-1, QuickBird, and WorldView2 satellite data sets in the subjective, objective, and efficiency aspects. The results show that the proposed method performs better than the compared methods in terms of higher spatial and spectral qualities.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Extinction Profiles Fusion for Hyperspectral Images Classification
    • Authors: Leyuan Fang;Nanjun He;Shutao Li;Pedram Ghamisi;Jón Atli Benediktsson;
      Pages: 1803 - 1815
      Abstract: An extinction profile (EP) is an effective spatial–spectral feature extraction method for hyperspectral images (HSIs), which has recently drawn much attention. However, the existing methods utilize the EPs in a stacking way, which is hard to fully explore the information in EPs for HSI classification. In this paper, a novel fusion framework termed EPs-fusion (EPs-F) is proposed to exploit the information within and among EPs for HSI classification. In general, EPs-F includes the following two stages. In the first stage, by extracting the EPs from three independent components of an HSI, three complementary groups of EPs can be constructed. For each EP, an adaptive superpixel-based composite kernel strategy is proposed to explore the spatial information within an EP. The weights to create the composite kernel and the number of superpixels are automatically determined based on the spatial information of each EP. In the second stage, since the different EPs contain highly complementary information, a simple yet effective decision fusion method is further applied to obtain the final classification result. Experiments on three real HSI data sets verify the qualitative and quantitative superiority of the proposed EPs-F method over several state-of-the-art HSI classifiers.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Bistatic Radar Systems at Large Baselines for Ocean Observation
    • Authors: Davide Comite;Nazzareno Pierdicca;
      Pages: 1816 - 1828
      Abstract: The capabilities of bistatic radar observations to estimate the wind field over the ocean are investigated in this paper. The work is based on the analysis of simulated data obtained through a well-established electromagnetic model, which accounts for the anisotropy of the ocean’s spectrum and of second-order effects of the scattering phenomenon. Both co-polarized and cross-polarized C-band numerical data, obtained considering monostatic and bistatic configurations, are exploited to investigate on the existence of optimal configurations able to minimize the wind vector error estimation. To this aim, the sensitivities of the bistatic normalized radar cross section with respect to both wind speed and direction are accurately investigated and exploited to evaluate the minimum achievable error standard deviation of the estimation. Small and large baselines are analyzed, giving particular emphasis to bistatic geometries constituted by one or two passive receivers aligned along the track defined by the active system. This investigation, originally performed in the framework of the SAOCOM-CS scientific satellite mission, is conceived to accurately assess the potentiality of bistatic observations of the ocean over variable baselines and to gather valuable information for the design of future bistatic satellite missions.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • GEO-Satellite-Based Reflectometry for Soil Moisture Estimation: Signal
           Modeling and Algorithm Development
    • Authors: Wei Ban;Kegen Yu;Xiaohong Zhang;
      Pages: 1829 - 1838
      Abstract: As a cost-effective remote sensing technique, global navigation satellite system reflectometry (GNSS-R) has recently drawn significant attention from both academia and industry. However, research on GNSS-R has mainly been focused on the global positioning system which consists of only medium earth orbit satellites, while the use of geostationary earth orbit (GEO) satellites, such as those in BeiDou navigation satellite system, has received little attention. This paper investigates the GEO-satellite-based GNSS-R with a focus on the application of soil moisture retrieval. Because GEO satellites remain static with the earth, the models of the reflected GNSS signals can be considerably simplified and their signals can be used to estimate soil moisture with a high update rate such as once per hour. Two new soil moisture estimation approaches using GEO signals are proposed, which are termed GEO interferometric reflectometry (GEO-IR) and GEO reflectometry (GEO-R). Two theoretical models (linear and second order) are developed for signal-to-noise ratio (SNR)-based GEO-IR as well as for phase-based GEO-IR. Meanwhile, two empirical models (linear and second order) are developed for signal amplitude-based GEO-R as well as for SNR ratio-based GEO-R. Experimental data sets collected from three different geographical regions were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-IR and GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
  • Unsupervised Fine Land Classification Using Quaternion Autoencoder-Based
           Polarization Feature Extraction and Self-Organizing Mapping
    • Authors: Hyunsoo Kim;Akira Hirose;
      Pages: 1839 - 1851
      Abstract: We propose an unsupervised polarimetric synthetic aperture radar (PolSAR) land classification system consisting of a series of two unsupervised neural networks, namely, a quaternion autoencoder and a quaternion self-organizing map (SOM). Most of the existing PolSAR land classification systems use a set of feature information that humans designed beforehand. However, such methods will face limitations in the near future when we expect classification into a large number of land categories recognizable to humans. By using a quaternion autoencoder, our proposed system extracts feature information based on the natural distribution of PolSAR features. In this paper, we confirm that the information necessary for land classification is extracted as the features while noise is filtered. Then, we show that the extracted features are classified by the quaternion SOM in an unsupervised manner. As a result, we can discover even new and more detailed land categories. For example, town areas are divided into residential areas and factory sites, and grass areas are subcategorized into furrowed farmlands and flat grass areas. We also examine the realization of topographic mapping of the features in the SOM space.
      PubDate: March 2018
      Issue No: Vol. 56, No. 3 (2018)
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