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Journal Cover Geoscience and Remote Sensing, IEEE Transactions on
  [SJR: 1.975]   [H-I: 168]   [166 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0196-2892
   Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Geoscience and Remote Sensing publication information
    • PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • IEEE Transactions on Geoscience and Remote Sensing information for authors
    • PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Multilayer Projective Dictionary Pair Learning and Sparse Autoencoder for
           PolSAR Image Classification
    • Authors: Yanqiao Chen;Licheng Jiao;Yangyang Li;Jin Zhao;
      Pages: 6683 - 6694
      Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse autoencoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Measurement of Ionospheric Scintillation Parameters From SAR Images Using
           Corner Reflectors
    • Authors: Christopher R. Mannix;David P. Belcher;Paul S. Cannon;
      Pages: 6695 - 6702
      Abstract: Space-based low-frequency (L-band and below) synthetic aperture radar (SAR) is affected by the ionosphere. In particular, the phase scintillation causes the sidelobes to rise in a manner that can be predicted by an analytical theory of the point spread function (PSF). In this paper, the results of an experiment, in which a 5 m corner reflector on Ascension Island, was repeatedly imaged by PALSAR-2 in the spotlight mode are described. Many examples of the effect of scintillation on the SAR PSF were obtained, and all fit the theoretical model. This theoretical model of the PSF has then been used to determine two ionospheric turbulence parameters p and $text {C}_{text {k}}text {L}$ from the SAR PSF. The values obtained have been compared with those obtained from simultaneous GPS measurements. Although the comparison shows that the two measures are strongly correlated, the differing spatial and temporal scales of SAR and GPS make exact comparison difficult.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Small Reflectors for Ground Motion Monitoring With InSAR
    • Authors: Prabu Dheenathayalan;Miguel Caro Cuenca;Peter Hoogeboom;Ramon F. Hanssen;
      Pages: 6703 - 6712
      Abstract: In recent years, synthetic aperture radar interferometry has become a recognized geodetic tool for observing ground motion. For monitoring areas with low density of coherent targets, artificial corner reflectors (CRs) are usually introduced. The required size of a reflector depends on radar wavelength and resolution and on the required deformation accuracy. CRs have been traditionally used to provide a high signal-to-clutter ratio (SCR). However, large dimensions can make the reflector bulky, difficult to install and maintain. Furthermore, if a large number of reflectors are needed for long infrastructure, such as vegetation-covered dikes, the total price of the reflectors can become unaffordable. On the other hand, small reflectors have the advantage of easy installation and low cost. In this paper, we design and study the use of small reflectors with low SCR for ground motion monitoring. In addition, we propose a new closed-form expression to estimate the interferometric phase precision of resolution cells containing a (strong or weak) point target and a clutter. Through experiments, we demonstrate that the small reflectors can also deliver displacement estimates with an accuracy of a few millimeters. To achieve this, we apply a filtering method for reducing clutter noise.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Detection and Estimation of Equatorial Spread F Scintillations Using
           Synthetic Aperture Radar
    • Authors: Jun Su Kim;Konstantinos P. Papathanassiou;Hiroatsu Sato;Shaun Quegan;
      Pages: 6713 - 6725
      Abstract: A significant amount of the data acquired by sun-synchronous space-borne low-frequency synthetic aperture radars (SARs) through the postsunset equatorial sector are distorted by the ionospheric scintillations due to the presence of plasma irregularities and their zonal and vertical drift. In the focused SAR images, the distortions due to the postsunset equatorial ionospheric scintillations appear in the form of amplitude and/or phase “stripe” patterns of high spatial frequency aligned to the projection of the geomagnetic field onto the SAR image plane. In this paper, a methodology to estimate the height and the drift velocity of the scintillations from the “stripe” patterns detected in the SAR images is proposed. The analysis is based on the fact that the zonal and vertical drift of the plasma irregularities are, at the equatorial zone, perpendicular to the geomagnetic field which is almost parallel aligned to the orbit. The methodology takes advantage of the time lapse and change of imaging geometry across azimuth subapertures. The obtained height estimates agree well with the reference measurements and independent estimates reported in the literature, while the drift velocities appear slightly overestimated. This can be attributed to a suboptimum geometry configuration but also to a decoupling of the ambient ionosphere and the plasma irregularities.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Spectral Radiance Modeling and Bayesian Model Averaging for Longwave
           Infrared Hyperspectral Imagery and Subpixel Target Identification
    • Authors: Blake M. Rankin;Joseph Meola;Michael T. Eismann;
      Pages: 6726 - 6735
      Abstract: Hyperspectral imagery (HSI) exploitation typically requires spectral signatures for target detection and identification algorithms. As the longwave infrared (LWIR) region of the electromagnetic spectrum is dominated by thermal emission, spectral radiance measurements are influenced by object temperature, and thus, estimates of target temperature may be necessary for emissivity retrieval to support these algorithms. Therefore, lack of accurate temperature information poses a significant challenge for HSI target detection and identification. Previous studies have demonstrated LWIR hyperspectral unmixing in both radiance and emissivity domains using in-scene target signatures. Here, a radiance-domain LWIR material identification algorithm for subpixel target identification of solid materials is developed by combining spectral radiance and linear mixing models with Bayesian model averaging. Application to experimental LWIR HSI illustrates that the algorithm effectively distinguishes between solid materials with a high degree of spectral similarity and reduces the probability of false alarms by at least one order of magnitude over a standard adaptive coherence estimator detector. Limits of identification are inferred from the imagery and found to depend on material type, target size, and target geometry. For the sensor and materials in this paper, the results imply that targets of nominally 5 m2 in size with strong spectral features can be identified for ground sampling distances (GSDs) on the order of 5–10 m (with abundances as low as ~10%) whereas blackbody-like materials are difficult to distinguish for GSDs larger than approximately 3 m.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • A Comparison of Feature Representations for Explosive Threat Detection in
           Ground Penetrating Radar Data
    • Authors: Rayn Sakaguchi;Kenneth D. Morton;Leslie M. Collins;Peter A. Torrione;
      Pages: 6736 - 6745
      Abstract: The automatic detection of buried threats in ground penetrating radar (GPR) data is an active area of research due to GPR’s ability to detect both metal and nonmetal subsurface objects. Recent work on algorithms designed to distinguish between threats and nonthreats in GPR data has utilized computer vision methods to advance the state-of-the-art detection and discrimination performance. Feature extractors, or descriptors, from the computer vision literature have exhibited excellent performance in representing 2-D GPR image patches and allow for robust classification of threats from nonthreats. This paper aims to perform a broad study of feature extraction methods in order to identify characteristics that lead to improved classification performance under controlled conditions. The results presented in this paper show that gradient-based features, such as the edge histogram descriptor and the scale invariant feature transform, provide the most robust performance across a large and varied data set. These results indicate that various techniques from the computer vision literature can be successfully applied to target detection in GPR data and that more advanced techniques from the computer vision literature may provide further performance improvements.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Combined Co- and Cross-Polarized SAR Measurements Under Extreme Wind
    • Authors: Alexis A. Mouche;Bertrand Chapron;Biao Zhang;Romain Husson;
      Pages: 6746 - 6755
      Abstract: During summer 2016, the European Space Agency (ESA) set up the Satellite Hurricane Observations Campaign, a campaign dedicated to hurricane observations with Sentinel-1 synthetic aperture radar (SAR) in both vertical-vertical (VV) and vertical-horizontal (VH) polarizations acquired in wide swath modes. Among the 70 Sentinel-1 passes scheduled by the ESA mission planning team, more than 20 observations over hurricane eyes were acquired and tropical cyclones were captured at different development stages. This enables us to detail the sensitivity difference of VH and VV normalized radar cross section (NRCS) to the response of intense ocean surface winds. As found, the sensitivity of the VH-NRCS computed at 3-km resolution is reported to be more than 3.5 times larger than in VV. Taking opportunity of SAR high resolution, we also show that the decrease in resolution (up to 25 km) does not dramatically change the sensitivity difference between VV and VH polarizations. For wind speeds larger than 25 m/s, a new geophysical model function (MS1A) to interpret cross-polarized signal is proposed. Both channels are then combined to get ocean surface wind vectors. SAR winds are further compared at 40-km resolution against L-band soil moisture active and passive mission (SMAP) radiometer winds with co-locations less than 30 min. Overall excellent consistency is found between SMAP and this new SAR winds. This paper opens perspectives for MetOp-SG SCA, the next-generation C-band scatterometer with co- and cross-polarization capability.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Remote Inspection of the Structural Integrity of Engineering Structures
           and Materials With Passive MST Probes
    • Authors: Massimo Donelli;Federico Viani;
      Pages: 6756 - 6766
      Abstract: This paper presents a method for the remote inspection of the structural integrity of engineering structures and materials, based on passive modulated scattering probes. In particular, a set of passive modulated scattering technique (MST) probes with diagnostic capabilities are embedded in structures, such as reinforced concrete slabs, with the objective of detecting water infiltrations, chemical deterioration, such as carbonation, and damages of the material structure. An external reader is used to provide the interrogating electromagnetic wave and to receive the signal generated by the MST probes. The material/structure integrity can be retrieved from the signal retransmitted by the probes. A set of preliminary experiments have been carried out to assess the potentialities of the method and to demonstrate how this system can be implemented for practical applications. The obtained results are quite promising.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Unsupervised Mixture-Eliminating Estimation of Equivalent Number of Looks
           for PolSAR Data
    • Authors: Dingsheng Hu;Stian Normann Anfinsen;Xiaolan Qiu;Anthony Paul Doulgeris;Bin Lei;
      Pages: 6767 - 6779
      Abstract: This paper addresses the impact of mixtures between classes on equivalent number of looks (ENL) estimation. We propose an unsupervised ENL estimator for polarimetric synthetic aperture radar (PolSAR) data, which is based on small sample estimates but incorporates a mixture-eliminating (ME) procedure to automatically assess the uniformity of the estimation windows. A statistical feature derived from a combination of linear and logarithmic moments is investigated and adopted in the procedure, as it has different mean values for samples from uniform and nonuniform windows. We introduce an approach to extract the approximated sampling distribution of this test statistic for uniform windows. Then the detection is conducted by a hypothesis test with adaptive thresholds determined by a nonuniformity ratio. Finally the experiments are performed on both simulated and real SAR data. The capability of the unsupervised ME procedure is verified with simulated data. In the real data experiments, the ENL estimates of Flevoland and San Francisco PolSAR images are analyzed, which show the robustness of the proposed ENL estimation for SAR scenes with different complexities.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals
    • Authors: Alexander Gruber;Wouter Arnoud Dorigo;Wade Crow;Wolfgang Wagner;
      Pages: 6780 - 6792
      Abstract: We propose a method for merging soil moisture retrievals from spaceborne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from using triple collocation analysis (TCA). In regions where TCA is deemed unreliable, we use correlation significance levels ( $p$ -values) as indicator for retrieval quality to decide whether to use active data only, passive data only, or an unweighted average. We apply the proposed merging scheme to active retrievals from advanced scatterometer and passive retrievals from the Advanced Microwave Scanning Radiometer—Earth Observing System using Global Land Data Assimilation System-Noah to complement the triplet required for TCA. The merged time series is evaluated against soil moisture estimates from ERA-Interim/Land and in situ measurements from the International Soil Moisture Network using the European Space Agency’s (ESA’s) current Climate Change Initiative—Soil Moisture (ESA CCI SM) product version v02.3 as benchmark merging scheme. Results show that the $p$ -value classification provides a robust basis for decisions regarding using either active or passive data alone, or an unweighted average in cases where relative weights cannot be estimated reliably, and that the weights estimated from TCA in almost all cases outperform the ternary decision upon which the ESA CCI SM v02.3 is based. The proposed method forms the basis for the new ESA CCI SM product version v03.x and higher.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Ice Detection for Satellite Ocean Color Data Processing in the Great Lakes
    • Authors: Seunghyun Son;Menghua Wang;
      Pages: 6793 - 6804
      Abstract: Satellite remote-sensing data are essential for monitoring and quantifying water properties in the Great Lakes, providing useful monitoring and management tools for understanding water optical, biological, and ecological processes and phenomena. However, during the winter season, large parts of the Great Lakes are often covered by ice, which can cause significant uncertainties in satellite-measured water quality products. Although some developed radiance-based ice-detection algorithms for satellite ocean color data processing can eliminate most of the ice pixels in a region, there are still some significant errors due to misidentification of ice-contaminated pixels, particularly for the thin ice-covered regions. Therefore, it is necessary to improve the ice-detection methods for satellite ocean color data processing in the Great Lakes. In this paper, impacts of ice contamination on satellite-derived ocean color products in the Great Lakes are investigated, and a refined regional ice-detection algorithm which is based on the radiance spectra and normalized water-leaving radiance at the wavelength of 551 nm, ${nL}_{{{w}}}$ (551), is developed and assessed for satellite ocean color data processing in the Great Lakes. Results show that this proposed ice-detection method can reasonably identify ice-contaminated pixels, including those in very thin ice-covered regions, and provide accurate satellite ocean color products for the winter season in the Great Lakes.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Unsupervised-Restricted Deconvolutional Neural Network for Very High
           Resolution Remote-Sensing Image Classification
    • Authors: Yiting Tao;Miaozhong Xu;Fan Zhang;Bo Du;Liangpei Zhang;
      Pages: 6805 - 6823
      Abstract: As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may ha-e produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Polarimetric Calibration of Circularly Polarized Synthetic Aperture Radar
    • Authors: Paul Pincus;Mark Preiss;Alvin S. Goh;Douglas Gray;
      Pages: 6824 - 6839
      Abstract: Two novel aspects of polarimetric calibration for fully polarimetric imaging radar systems are addressed. First, the radar system model is formulated in the context of two generic transmitter designs, either a single amplifier followed by a high-power switch or a low-power switch followed by two amplifiers. In the latter case, it is shown that a particular factorization of the polarimetric distortion matrix leads to a significant simplification of the cross-talk representation, from the standard four parameters to two reciprocal parameters, one for each of the antennas. Various system models from the literature are thus placed in a unified framework. Second, calibration techniques for circularly polarized antennas are derived, using either corner reflectors or clutter. However, where standard linear-basis algorithms estimate the cross-talk by its first-order distortion of reflection-symmetric clutter, no equivalent algorithm has been found for the circular basis; indeed, it is shown that the distortion caused, to first-order, by circular-basis cross-talk does not permit the individual cross-talk parameters to be identified. The calibration techniques are applied to fully polarimetric data acquired by the Ingara L-band radar using left- and right-polarized helical antennas.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • A Simple and Efficient Method for Radial Distortion Estimation by Relative
    • Authors: Yansong Duan;Xiao Ling;Yongjun Zhang;Zuxun Zhang;Xinyi Liu;Kun Hu;
      Pages: 6840 - 6848
      Abstract: In order to solve the accuracy problem caused by lens distortions of nonmetric digital cameras mounted on an unmanned aerial vehicle, the estimation for initial values of lens distortion must be studied. Based on the fact that radial lens distortions are the most significant of lens distortions, a simple and efficient method for radial lens distortion estimation is proposed in this paper. Starting from the coplanar equation, the geometric characteristics of the relative orientation equations are explored. This paper further proves that the radial lens distortion can be linearly estimated in a continuous relative orientation model. The proposed procedure only requires a sufficient number of point correspondences between two or more images obtained by the same camera; thus it is suitable for a natural scene where the lack of straight lines and calibration objects precludes most previous techniques. Both computer simulation and real data have been used to test the proposed method; the experimental results show that the proposed method is easy to use and flexible.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Evaluation of Rainfall Products Derived From Satellites and Microwave
           Links for The Netherlands
    • Authors: Manuel F. Rios Gaona;Aart Overeem;A. M. Brasjen;Jan Fokke Meirink;Hidde Leijnse;Remko Uijlenhoet;
      Pages: 6849 - 6859
      Abstract: High-resolution inputs of rainfall are important in hydrological sciences, especially for urban hydrology. This is mainly because heavy rainfall-induced events such as flash floods can have a tremendous impact on society given their destructive nature and the short time scales in which they develop. With the development of technologies such as radars, satellites and (commercial) microwave links (CMLs), the spatiotemporal resolutions at which rainfall can be retrieved are becoming higher and higher. For the land surface of The Netherlands, we evaluate here four rainfall products, i.e., link-derived rainfall maps, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG—Global Precipitation Measurement mission), Meteosat Second Generation Cloud Physical Properties (CPP), and Nighttime Infrared Precipitation Estimation (NIPE). All rainfall products are compared against gauge-adjusted radar data, considered as the ground truth given its high quality, resolution, and availability. The evaluation is done for seven months at 30 min and 24h. Overall, we found that link-derived rainfall maps outperform the satellite products and that IMERG outperforms CPP and NIPE. We also explore the potential of a CML network to validate satellite rainfall products. Usually, satellite derived products are validated against radar or rain gauge networks. If data from CMLs would be available, this would be highly relevant for ground validation in areas with scarce rainfall observations, since link-derived rainfall is truly independent of satellite-derived rainfall. The large worldwide coverage of CMLs potentially offers a more extensive platform for the ground validation of satellite estimates over the land surface of the Earth.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Structured Sparse Coding-Based Hyperspectral Imagery Denoising With
           Intracluster Filtering
    • Authors: Wei Wei;Lei Zhang;Chunna Tian;Antonio Plaza;Yanning Zhang;
      Pages: 6860 - 6876
      Abstract: Sparse coding can exploit the intrinsic sparsity of hyperspectral images (HSIs) by representing it as a group of sparse codes. This strategy has been shown to be effective for HSI denoising. However, how to effectively exploit the structural information within the sparse codes (structured sparsity) has not been widely studied. In this paper, we propose a new method for HSI denoising, which uses structured sparse coding and intracluster filtering. First, due to the high spectral correlation, the HSI is represented as a group of sparse codes by projecting each spectral signature onto a given dictionary. Then, we cast the structured sparse coding into a covariance matrix estimation problem. A latent variable-based Bayesian framework is adopted to learn the covariance matrix, the sparse codes, and the noise level simultaneously from noisy observations. Although the considered strategy is able to perform denoising through accurately reconstructing spectral signatures, an inconsistent recovery of sparse codes may corrupt the spectral similarity in each spatial homogeneous cluster within the scene. To address this issue, an intracluster filtering scheme is further employed to restore the spectral similarity in each spatial cluster, which results in better denoising results. Our experimental results, conducted using both simulated and real HSIs, demonstrate that the proposed method outperforms several state-of-the-art denoising methods.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Atmospheric Boundary Layer Characterization Using Multiyear Ground-Based
           Microwave Radiometric Observations Over a Tropical Coastal Station
    • Authors: R. Renju;C. Suresh Raju;M. K. Mishra;N. Mathew;K. Rajeev;K. Krishna Moorthy;
      Pages: 6877 - 6882
      Abstract: The continuous ground-based microwave radiometer profiler (MRP) observations of lower atmospheric temperature and humidity profiles are used to investigate the diurnal evolution of atmospheric boundary layer height (BLH) over a tropical coastal station. The BLH estimated from the MRP observations is compared with concurrent and collocated measurements of mixing layer height using a Micropulse Lidar and the BLH derived from radiosonde ascends. The monthly mean diurnal variation of the BLH derived from the multiyear (2010–2013) MRP observations exhibits strong diurnal variation with the highest around the local afternoon (~12:00–15:00 IST) and the lowest during the nighttime (~100–200 m). The daytime convective BLH is maximum during the premonsoon season (March–May) with the peak value (~1300 m) occurring in April and minimum in the month of July (~600 m). This paper presents the potential of MRP observations to investigate the continuous diurnal evolution of the BLH over a tropical coastal region manifested by a thermal internal boundary layer (TIBL) at much better time resolution, which is essential for understanding the rapid growth of the boundary layer and the TIBL during the forenoon period.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • On the Amplitude Distributions of Bistatic Scattered Fields From Rough
    • Authors: Hongkun Li;Joel T. Johnson;
      Pages: 6883 - 6892
      Abstract: Non-Rayleigh distributed radar clutter is widely reported in studies of radar scattering from sea and land surfaces. Existing models of scattered field amplitude distributions have been developed primarily through empirical fits to the statistics of radar backscatter measurements. In contrast, this paper investigates a physics-based approach to determine the amplitude distributions of fields scattered from rough surfaces using Monte Carlo simulations and analytical methods, for both backscattering and bistatic configurations. The rough surface is represented using a “two-scale” model. An individual surface facet contains “small-scale” roughness, for which scattered fields are evaluated using the second-order small slope approximation. Individual surface facets are tilted by the slopes of the “large-scale” roughness in a given observation. The results show that non-Rayleigh amplitude distributions are obtained when tilting is performed, and that the departure from the Rayleigh distribution becomes more significant as the variance of the tilting slope increases. Further analysis shows that this departure results from variations in the mean scattering amplitude from a facet (the texture) as tilting occurs. The distribution of the texture is studied and compared with existing models. Finally, the distribution of the scattered field amplitude is modeled through the compound Gaussian model, first using the distribution of the texture, and then in terms of the probability density function of tilting slopes (which avoids the requirement of the knowledge of the texture distribution). The results from the above two methods are in good agreement and both agree well with the Monte Carlo simulation.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Evaluation of the New Information in the ${H}/alpha$ Feature Space
           Provided by ICA in PolSAR Data Analysis
    • Authors: Leandro Pralon;Gabriel Vasile;Mauro Dalla Mura;Jocelyn Chanussot;
      Pages: 6893 - 6909
      Abstract: The Cloude and Pottier $H/alpha $ feature space is one of the most employed methods for unsupervised polarimetric synthetic aperture radar (PolSAR) data classification based on incoherent target decomposition (ICTD). The method can be split in two stages: the retrieval of the canonical scattering mechanisms present in an image cell and their parameterization. The association of the coherence matrix eigenvectors to the most dominant scattering mechanisms in the analyzed pixel introduces unfeasible regions in the $H/alpha $ plane. This constraint can compromise the performance of detection, classification, and geophysical parameter inversion algorithms that are based on the investigation of this feature space. The independent component analysis (ICA), recently proposed as an alternative to eigenvector decomposition, provides promising new information to better interpret non-Gaussian heterogeneous clutter (inherent to high-resolution SAR systems) in the frame of polarimetric ICTDs. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the $H/alpha $ plane, increasing the range of possible natural phenomena depicted in the aforementioned feature space. This paper addresses the potential of the new information provided by the ICA as an ICTD method with respect to Cloude and Pottier $H/alpha $ feature space. A PolSAR data set acquired in October 2006 by the E-SAR system over the upper part of the Tacul glacier from the Chamonix Mont Blanc test site, France, and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to in-estigate the characteristics of pixels that may fall outside the feasible regions in the $H/alpha $ plane that arise when the eigenvector approach is employed.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • A Modified Three-Step Algorithm for TOPS and Sliding Spotlight SAR Data
    • Authors: Wei Yang;Jie Chen;Wei Liu;Pengbo Wang;Chunsheng Li;
      Pages: 6910 - 6921
      Abstract: There are two challenges for efficient processing of both the sliding spotlight and terrain observation by progressive scans (TOPS) data using full-aperture algorithms. First, to overcome the Doppler spectrum aliasing, zero-padding is required for azimuth up sampling, increasing the computation burden; second, the azimuth deramp operation for avoiding synthetic aperture radar (SAR) image folding leads to azimuth time shift along the range dimension, and in turn the appearance of ghost targets and azimuth resolution reduction at the scene edge, especially in the wide-swath case. In this paper, a novel three-step algorithm is proposed for processing the sliding spotlight and TOPS data. In the first step, a modified derotation is derived in detail based on the chirp z-transform (CZT), avoiding zero-padding; then, the chirp scaling algorithm kernel is adopted for precise focusing in the second step; and in the third step, instead of the traditional range-independent deramp, a range-dependent deramp is applied to compensate for the time shift. Moreover, the SAR image geometry distortion caused by range-dependent deramp is corrected by employing a range-dependent CZT. Experimental results based on both simulated data and real data are provided to validate the proposed algorithm.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Wide Nonlinear Chirp Scaling Algorithm for Spaceborne Stripmap Range Sweep
           SAR Imaging
    • Authors: Yan Wang;Jing-Wen Li;Jian Yang;
      Pages: 6922 - 6936
      Abstract: The spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is a new concept spaceborne SAR system that images the region of interest (ROI) with ROI-orientated strips, which, unlike the traditional spaceborne SAR, are allowed to be not parallel with the satellite orbit. The SS-RSSAR imaging is a challenging problem because echoes of a wide region have strong spatial varieties, especially in high-squint geometries, and are hard to be focused by a single swath. The traditional imaging algorithms could solve this problem by cost-ineffectively dividing an ROI into many subswaths for separate processing. In this paper, a new wide nonlinear chirp scaling (W-NLCS) algorithm is proposed to efficiently image the SS-RSSAR data in a single swath. Comparing with the traditional nonlinear chirp scaling algorithm, the W-NLCS algorithm is superior in three major aspects: the nonlinear bulk range migration compensation (RMC), the interpolation-based residual RMC, and the modified azimuth frequency perturbation. Specifically, the interpolation for the residual RMC, the most significant step in achieving the wide-swath imaging performance, is made innovatively in the time domain. The derivation of the W-NLCS algorithm, as well as the performance analyses of the W-NLCS algorithm in aspects of the azimuth resolution, accuracy, and complexity, are all provided. The presented approach is evaluated by the point target simulations.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • OSSIM: An Object-Based Multiview Stereo Algorithm Using SSIM Index
           Matching Cost
    • Authors: Liang Fei;Li Yan;Changhai Chen;Zhiyun Ye;Jiantong Zhou;
      Pages: 6937 - 6949
      Abstract: Multiview stereo (MVS) is a crucial process in image-based automatic 3-D reconstruction and mapping applications. In a dense matching process, the matching cost is generally computed between image pairs, making the efficiency low due to the large number of stereo pairs. This paper presents a novel object-based MVS algorithm using structural similarity (SSIM) index matching cost in a coarse-to-fine workflow. As far as we know, this is the first time SSIM index is introduced to calculate the matching cost of MVS applications. In contrast to classical stereo methods, the proposed object-based structural similarity (OSSIM) method computes only a depth map for each image. Thus, the efficiency can be greatly improved when the overlap between images is large. To obtain an optimized depth map, the winner-take-all and semi-global matching strategies are implemented. Moreover, an object-based multiview consistency checking strategy is also proposed to eliminate wrong matches and perform pixelwise view selection. The proposed method was successfully applied on a close-range Fountain-P11 data set provided by EPFL and aerial data sets of Vaihingen and Zürich by the ISPRS. Experimental results demonstrate that the proposed method can deliver matches at high completeness and accuracy. For the Vaihingen data set, the correctness and completeness rate were 71.12% and 95.99% with an RMSE of 2.8 GSD. For the Foutain-P11 data set, the proposed method outperformed the other existing methods with the ratio of pixels less than 2 cm. Extensive comparison using Zürich data set shows that it can derive results comparable to the state-of-the-art software (PhotoScan, Pix4d, and Smart3D) in urban buildings areas.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Multimorphological Superpixel Model for Hyperspectral Image Classification
    • Authors: Tianzhu Liu;Yanfeng Gu;Jocelyn Chanussot;Mauro Dalla Mura;
      Pages: 6950 - 6963
      Abstract: With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • L-Band Model Function of the Dielectric Constant of Seawater
    • Authors: Yiwen Zhou;Roger H. Lang;Emmanuel P. Dinnat;David M. Le Vine;
      Pages: 6964 - 6974
      Abstract: This paper describes a new model of the seawater dielectric constant as a function of salinity and temperature at L-band. The model function is developed by fitting the accurate measurement data made at 1.413 GHz to a third-order polynomial. The purpose of this study is to provide an accurate model for earth-observing satellites to retrieve seawater salinities from remote sensing data. In this paper, the development of the model function is introduced along with an analysis of the goodness of fit. The model function is then compared with the model functions of Klein–Swift and Meissner–Wentz. Finally, the comparison is made between the retrieved salinity from the satellite data with the in situ data measured by Argo floats.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • A Hierarchical Split-Based Approach for Parametric Thresholding of SAR
           Images: Flood Inundation as a Test Case
    • Authors: Marco Chini;Renaud Hostache;Laura Giustarini;Patrick Matgen;
      Pages: 6975 - 6988
      Abstract: Parametric thresholding algorithms applied to synthetic aperture radar (SAR) imagery typically require the estimation of two distribution functions, i.e., one representing the target class and one its background. They are eventually used for selecting the threshold that allows binarizing the image in an optimal way. In this context, one of the main difficulties in parameterizing these functions originates from the fact that the target class often represents only a small fraction of the image. Under such circumstances, the histogram of the image values is often not obviously bimodal and it becomes difficult, if not impossible, to accurately parameterize distribution functions. Here we introduce a hierarchical split-based approach that searches for tiles of variable size allowing the parameterization of the distributions of two classes. The method is integrated into a flood-mapping algorithm in order to evaluate its capacity for parameterizing distribution functions attributed to floodwater and changes caused by floods. We analyzed a data set acquired during a flood event along the Severn River (U.K.) in 2007. It is composed of moderate (ENVISAT-WS) and high (TerraSAR-X)-resolution SAR images. The obtained classification accuracies as well as the similarity of performance levels to a benchmark obtained with an established method based on the manual selection of tiles indicate the validity of the new method.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • An Object-Based Linear Weight Assignment Fusion Scheme to Improve
           Classification Accuracy Using Landsat and MODIS Data at the Decision Level
    • Authors: Xudong Guan;Gaohuan Liu;Chong Huang;Qingsheng Liu;Chunsheng Wu;Yan Jin;Yafei Li;
      Pages: 6989 - 7002
      Abstract: Landsat satellite images are extensively used in land-use studies due to their relatively high spatial resolution. However, the number of usable data sets is limited by the relatively long revisit interval and phenology effects can significantly reduce classification accuracy. Moderate Resolution Imaging Spectroradiometer (MODIS) images have higher temporal frequency and can provide extra time-series information. However, they are limited in their capability to classify heterogeneous landscapes due to their coarse spatial resolution. Fusion of different data sources is a potential solution for improving land-cover classification. This paper proposes a fusion scheme to combine Landsat and MODIS remote sensing data at the decision level. First, multiresolution segmentations on the two kinds of remote sensing data are performed to identify the landscape objects and are used as fusion units in subsequent steps. Then, fuzzy classifications are applied to each of the two different resolution data sets and the classification accuracies are evaluated. According to the performance of the two data sets in classification evaluation, a simple weight assignment technique based on the weighted sum of the membership of imaged objects is implemented in the final classification decision. The weighting factors are calculated based on a confusion matrix and the heterogeneity of detected land cover. The algorithm is capable of integrating the time-series spectral information of MODIS data with spatial contexts extracted from Landsat data, thus improving the land-cover classification accuracy. The overall classification accuracy using the fusion technique increased by 7.43% and 10.46% compared with the results from the individual Landsat and MODIS data, respectively.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Weighted Spectral-Spatial Classification of Hyperspectral Images via
           Class-Specific Band Contribution
    • Authors: Chenhong Sui;Yan Tian;Yiping Xu;Yong Xie;
      Pages: 7003 - 7017
      Abstract: Hyperspectral images (HSIs) have evident advantages in image understanding due to enormous spectral bands, and rich spatial information. Hundreds of spectral bands, however, actually play different roles in contributing to the class-specific classification. Then, treating each band equally may lead to the underuse or overuse of them. To address this issue, this paper introduces class-specific band contributions (BCs) into the spectral space, and proposes a weighted spectral-spatial classification method for HSIs. In the method, by incorporating BC characterized by F-measure into the distance-based posterior probability, a weighted spectral posterior probability (WSP) model is established. Furthermore, to exploit the spatial information, WSP is then combined with the spatial consistency constraint via an adaptive tradeoff parameter. Additionally, aimed at obtaining the class-dependent F-measures of each band, a semisupervised F-measure prediction method is also developed. Experiments on four hyperspectral data sets are conducted. Experimental results show the superiority of our proposed method over several state-of-the-art methods in terms of three widely used indexes.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Estimating Azimuth Offset With Double-Difference Interferometric Phase:
           The Effect of Azimuth FM Rate Error in Focusing
    • Authors: Cunren Liang;Eric J. Fielding;Mong-Han Huang;
      Pages: 7018 - 7031
      Abstract: Estimating azimuth offset with double-difference interferometric (DDI) phase, which is called multiple-aperture interferometric synthetic aperture radar (InSAR) or spectral diversity, is increasingly used in recent years to measure azimuth deformation or to accurately coregister a pair of InSAR images. We analyze the effect of frequency modulation (FM) rate error in focusing on the DDI phase with an emphasis on the azimuth direction. We first comprehensively analyze the errors in various focusing results caused by the FM rate error. We then derive the DDI phase error considering different acquisition modes including stripmap, ScanSAR, and TOPS modes. For stripmap mode, typical DDI phase error is a range ramp, while for burst modes including ScanSAR and TOPS modes it is an azimuth ramp within a burst. The correction methods for the DDI phase error are suggested for different acquisition modes.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Topologically Aware Building Rooftop Reconstruction From Airborne Laser
           Scanning Point Clouds
    • Authors: Dong Chen;Ruisheng Wang;Jiju Peethambaran;
      Pages: 7032 - 7052
      Abstract: This paper presents a novel topologically aware 2.5-D building modeling methodology from airborne laser scanning point clouds. The building reconstruction process consists of three main steps: primitive clustering, boundary representation, and geometric modeling. In primitive clustering, we propose an enhanced probability density clustering algorithm to cluster the rooftop primitives by taking into account the topological consistency among primitives. In the second step, we employ a novel Voronoi subgraph-based algorithm to seamlessly trace the primitive boundaries. This algorithm guarantees the production of geometric models without crack defects among adjacent primitives. The primitive boundaries are further divided into multiple linear segments, from which the key points are generated. These key points help to form a hybrid representation of the boundary by combining the projected points with part of the original boundary points. The model representation by the hybrid key points is flexible and well captures the rooftop details to generate lightweight and highly regular building models. Finally, we assemble the primitive boundaries to form the topologically correct entities, which are regarded as the basic units for primitive triangulation. The reconstructed models not only have accurate geometry and correct topology but more importantly have abundant semantics, by which five levels of building models can be generated in real time. The proposed reconstruction method has been comprehensively evaluated on Toronto data set in terms of model compactness, multilevel model representation, and geometric accuracy.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Quasi-Polar-Based FFBP Algorithm for Miniature UAV SAR Imaging Without
           Navigational Data
    • Authors: Song Zhou;Lei Yang;Lifan Zhao;Guoan Bi;
      Pages: 7053 - 7065
      Abstract: Because of flexible geometric configuration and trajectory designation, time-domain algorithms become popular for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) applications. In this paper, a new quasi-polar-coordinate-based fast factorized back-projection (FFBP) algorithm combined with data-driven motion compensation is proposed for miniature UAV-SAR imaging. By utilizing wavenumber decomposition, the analytical spectrum of a quasi-polar grid image is obtained, where the phase errors arising from the trajectory deviations can be conveniently investigated and the phase autofocusing can be compatibly incorporated. Different from the conventional FFBP based on a polar coordinate system, the proposed algorithm operates in a quasi-polar coordinate system, where the phase errors become spacial invariant and can be accurately estimated and easily compensated. Moreover, the relationship between phase errors and nonsystematic range cell migration (NsRCM) is revealed according to the analytical image spectrum, based on which the NsRCM correction is developed to further improve the image focusing quality for high-resolution SAR applications. Promising experimental results from the raw data experiments of miniature UAV-SAR test bed are presented and analyzed to validate the advantages of the proposed algorithm.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Discriminative Feature Learning for Unsupervised Change Detection in
           Heterogeneous Images Based on a Coupled Neural Network
    • Authors: Wei Zhao;Zhirui Wang;Maoguo Gong;Jia Liu;
      Pages: 7066 - 7080
      Abstract: With the application requirement, the technique for change detection based on heterogeneous remote sensing images is paid more attention. However, detecting changes between two heterogeneous images is challenging as they cannot be compared in low-dimensional space. In this paper, we construct an approximately symmetric deep neural network with two sides containing the same number of coupled layers to transform the two images into the same feature space. The two images are connected with the two sides and transformed into the same feature space, in which their features are more discriminative and the difference image can be generated by comparing paired features pixel by pixel. The network is first built by stacked restricted Boltzmann machines, and then, the parameters are updated in a special way based on clustering. The special way, motivated by that two heterogeneous images share the same reality in unchanged areas and retain respective properties in changed areas, shrinks the distance between paired features transformed from unchanged positions, and enlarges the distance between paired features extracted from changed positions. It is achieved through introducing two types of labels and updating parameters by adaptively changed learning rate. This is different from the existing methods based on deep learning that just do operations on positions predicted to be unchanged and extract only one type of labels. The whole process is completely unsupervised without any priori knowledge. Besides, the method can also be applied to homogeneous images. We test our method on heterogeneous images and homogeneous images. The proposed method achieves quite high accuracy.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Modified Chirp Scaling Algorithm for Circular Trace Scanning Synthetic
           Aperture Radar
    • Authors: Yi Liao;Qing Huo Liu;
      Pages: 7081 - 7091
      Abstract: For circular trace scanning synthetic aperture radar (CTSSAR) with a circular track, the conventional hyperbolic equation becomes inadequate to express the range history of a point target accurately, and when it comes to the wide swath observation and imaging, the range variance makes it even harder to focus the target on the edge of the scene. Thus, an expression with high-order terms is needed to approximate the range history and the range variance should also be considered in the imaging algorithm. In this paper, based on the method of series reversion, a fourth-order approximated range model is established for the CTSSAR processing and the 2-D spectrum is derived for the echo signal in CTSSAR with circular trajectory. At the same time, in order to deal with the range-variant range cell migration problem in large-area CTSSAR imaging, a modified chirp scaling algorithm is proposed to realize precise wide swath CTSSAR focusing. Experiments and analyses are performed to validate the effectiveness of the proposed algorithm.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • High-Resolution Aerial Image Labeling With Convolutional Neural Networks
    • Authors: Emmanuel Maggiori;Yuliya Tarabalka;Guillaume Charpiat;Pierre Alliez;
      Pages: 7092 - 7103
      Abstract: The problem of dense semantic labeling consists in assigning semantic labels to every pixel in an image. In the context of aerial image analysis, it is particularly important to yield high-resolution outputs. In order to use convolutional neural networks (CNNs) for this task, it is required to design new specific architectures to provide fine-grained classification maps. Many dense semantic labeling CNNs have been recently proposed. Our first contribution is an in-depth analysis of these architectures. We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties. We observe that even though they provide competitive results, these CNNs often underexploit properties of semantic labeling that could lead to more effective and efficient architectures. Out of these observations, we then derive a CNN framework specifically adapted to the semantic labeling problem. In addition to learning features at different resolutions, it learns how to combine these features. By integrating local and global information in an efficient and flexible manner, it outperforms previous techniques. We evaluate the proposed framework and compare it with state-of-the-art architectures on public benchmarks of high-resolution aerial image labeling.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Brightness Temperature Computation of Microwave Calibration Targets
    • Authors: Arne Schröder;Axel Murk;Richard Wylde;Dennis Schobert;Mike Winser;
      Pages: 7104 - 7112
      Abstract: A rigorous numerical technique to compute the brightness temperature of arbitrarily shaped microwave calibration targets is presented. The proposed method allows the brightness temperature of calibration targets to be investigated depending on frequency, absorber material, geometry, antenna pattern, field incidence, and temperature environment. We have validated the accuracy and studied the numerical complexity of the approach by means of analytical reference solutions. Fundamental brightness temperature investigations of pyramid absorbers are shown for various thermal environments in different frequency bands between 20 and 450 GHz. Based on these analyses, a novel pyramid geometry was designed, which features a superior electromagnetic and thermal performance compared with conventional pyramid designs. Using the theoretical findings, we have developed reduced-order models of pyramid targets for rapid brightness temperature studies.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Computing Ocean Surface Currents From GOCI Ocean Color Satellite Imagery
    • Authors: Jianfei Liu;William J. Emery;Xiongbin Wu;Miao Li;Chuan Li;Lan Zhang;
      Pages: 7113 - 7125
      Abstract: One of the significant challenges in physical oceanography is getting an adequate space/time description of the ocean surface currents. One possible solution is the maximum cross-correlation (MCC) method that we apply to hourly ocean color images from the Geostationary Ocean Color Imager (GOCI) over five years. Since GOCI provided a large number of image pairs, we introduce a new MCC search strategy to improve the computational efficiency of the MCC method saving 95% of the processing time. We also use an MCC current merging method to increase the total spatial coverage of the currents, proving a 25% increase. Five-year mean and seasonal time-average flows are computed to capture the major currents in the area of interest. The mean flows investigate the Kuroshio path, support the triple-branch pattern of the Tsushima Warm Current (TC), and reveal the origin of the TC. The evolution of a warm core ring shed by the Kuroshio near the northeast coast of Honshu, Japan, is clearly depicted by a sequence of three monthly MCC composites. We capture the evolution of the Kuroshio meander over seasonal, monthly, and weekly time scales. Three successive weekly MCC composite maps demonstrate how a large anticyclonic eddy, to the south of the Kuroshio meander, influences its formation and evolution in time and space. The unique ability to view short space/time scale changes in these strong current systems is a major benefit of the application of the MCC method to the high spatial resolution and rapid refresh GOCI data.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Spatiotemporal Fusion of MODIS and Landsat-7 Reflectance Images via
           Compressed Sensing
    • Authors: Jingbo Wei;Lizhe Wang;Peng Liu;Xiaodao Chen;Wei Li;Albert Y. Zomaya;
      Pages: 7126 - 7139
      Abstract: The fusion of remote sensing images with different spatial and temporal resolutions is needed for diverse Earth observation applications. A small number of spatiotemporal fusion methods that use sparse representation appear to be more promising than weighted- and unmixing-based methods in reflecting abruptly changing terrestrial content. However, none of the existing dictionary-based fusion methods consider the downsampling process explicitly, which is the degradation and sparse observation from high-resolution images to the corresponding low-resolution images. In this paper, the downsampling process is described explicitly under the framework of compressed sensing for reconstruction. With the coupled dictionary to constrain the similarity of sparse coefficients, a new dictionary-based spatiotemporal fusion method is built and named compressed sensing for spatiotemporal fusion, for the spatiotemporal fusion of remote sensing images. To deal with images with a high-resolution difference, typically Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS), the proposed model is performed twice to shorten the gap between the small block size and the large resolution rate. In the experimental procedure, the near-infrared, red, and green bands of Landsat-7 and MODIS are fused with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than five state-of-the-art methods, which prove the feasibility of incorporating the downsampling process in the spatiotemporal model under the framework of compressed sensing.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • PCA-Based Edge-Preserving Features for Hyperspectral Image Classification
    • Authors: Xudong Kang;Xuanlin Xiang;Shutao Li;Jón Atli Benediktsson;
      Pages: 7140 - 7151
      Abstract: Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to hyperspectral images (HSIs) have been found very effective in characterizing significant spectral and spatial structures of objects in a scene. However, a direct use of the EPFs can be insufficient to provide a complete characterization of spatial information when objects of different scales are present in the considered images. Furthermore, the edge-preserving smoothing operation unavoidably decreases the spectral differences among objects of different classes, which may affect the following classification. To overcome these problems, in this paper, a novel principal component analysis (PCA)-based EPFs (PCA-EPFs) method for HSI classification is proposed, which consists of the following steps. First, the standard EPFs are constructed by applying edge-preserving filters with different parameter settings to the considered image, and the resulting EPFs are stacked together. Next, the spectral dimension of the stacked EPFs is reduced with the PCA, which not only can represent the EPFs in the mean square sense but also highlight the separability of pixels in the EPFs. Finally, the resulting PCA-EPFs are classified by a support vector machine (SVM) classifier. Experiments performed on several real hyperspectral data sets show the effectiveness of the proposed PCA-EPFs, which sharply improves the accuracy of the SVM classifier with respect to the standard edge-preserving filtering-based feature extraction method, and other widely used spectral-spatial classifiers.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Robust Joint Sparse Representation Based on Maximum Correntropy Criterion
           for Hyperspectral Image Classification
    • Authors: Jiangtao Peng;Qian Du;
      Pages: 7152 - 7164
      Abstract: Joint sparse representation (JSR) has been a popular technique for hyperspectral image classification, where a testing pixel and its spatial neighbors are simultaneously approximated by a sparse linear combination of all training samples, and the testing pixel is classified based on the joint reconstruction residual of each class. Due to the least-squares representation of the approximation error, the JSR model is usually sensitive to outliers, such as background, noisy pixels, and outlying bands. In order to eliminate such effects, we propose three correntropy-based robust JSR (RJSR) models, i.e., RJSR for handling pixel noise, RJSR for handling band noise, and RJSR for handling both pixel and band noise. The proposed RJSR models replace the traditional square of the Euclidean distance with the correntropy-based metric in measuring the joint approximation error. To solve the correntropy-based joint sparsity model, a half-quadratic optimization technique is developed to convert the original nonconvex and nonlinear optimization problem into an iteratively reweighted JSR problem. As a result, the optimization of our models can handle the noise in neighboring pixels and the noise in spectral bands. It can adaptively assign small weights to noisy pixels or bands and put more emphasis on noise-free pixels or bands. The experimental results using real and simulated data demonstrate the effectiveness of our models in comparison with the related state-of-the-art JSR models.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Applying Model Parameters as a Driving Force to a Deterministic Nonlinear
           System to Detect Land Cover Change
    • Authors: B. P. Salmon;D. S. Holloway;W. Kleynhans;J. C. Olivier;K. J. Wessels;
      Pages: 7165 - 7176
      Abstract: In this paper, we propose a new method for extracting features from time-series satellite data to detect land cover change. We propose to make use of the behavior of a deterministic nonlinear system driven by a time-dependent force. The driving force comprises a set of concatenated model parameters regressed from fitting a model to a Moderate Resolution Imaging Spectroradiometer time series. The goal is to create behavior in the nonlinear deterministic system, which appears predictable for the time series undergoing no change, while erratic for the time series undergoing land cover change. The differential equation used for the deterministic nonlinear system is that of a large-amplitude pendulum, where the displacement angle is observed over time. If there has been no change in the land cover, the mean driving force will approximate zero, and hence the pendulum will behave as if in free motion under the influence of gravity only. If, however, there has been a change in the land cover, this will for a brief initial period introduce a nonzero mean driving force, which does work on the pendulum, changing its energy and future evolution, which we demonstrate is observable. This we show is sufficient to introduce an observable change to the state of the pendulum, thus enabling change detection. We extend this method to a higher dimensional differential equation to improve the false alarm rate in our experiments. Numerical results show a change detection accuracy of nearly 96% when detecting new human settlements, with a corresponding false alarm rate of 0.2% (omission error rate of 4%). This compares very favorably with other published methods, which achieved less than 90% detection but with false alarm rates all above 9% (omission error rate of 66%).
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Complex-Valued Convolutional Neural Network and Its Application in
           Polarimetric SAR Image Classification
    • Authors: Zhimian Zhang;Haipeng Wang;Feng Xu;Ya-Qiu Jin;
      Pages: 7177 - 7188
      Abstract: Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Subgridded FDTD Modeling of Ground Penetrating Radar Scenarios Beyond the
           Courant Stability Limit
    • Authors: Xiao-Kun Wei;Xingqi Zhang;Nectaria Diamanti;Wei Shao;Costas D. Sarris;
      Pages: 7189 - 7198
      Abstract: This paper presents an efficient 3-D finite-difference time-domain (FDTD) subgridding scheme that is free of the Courant–Friedrichs–Lewy stability condition, for the modeling of ground-penetrating radar (GPR) scenarios in lossy dispersive media. Spatial filtering of FDTD fields within the subgrid is employed to render the time step independent of the cell size in the fine-cell subgrids. This process is applied with minimal modification of the original FDTD code, no implicit operations, and very modest computational overhead. Moreover, multiterm dispersion is included to model practical GPR scenarios involving the detection of realistic scatterers within dispersive soil. Several numerical examples are provided to demonstrate the potential of the proposed method as a powerful GPR modeling tool.
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
  • Introducing IEEE Collabratec
    • Pages: 7199 - 7199
      PubDate: Dec. 2017
      Issue No: Vol. 55, No. 12 (2017)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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