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IEEE Geoscience and Remote Sensing Letters
Journal Prestige (SJR): 1.486
Citation Impact (citeScore): 4
Number of Followers: 152  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1545-598X
Published by IEEE Homepage  [191 journals]
  • IEEE Geoscience and Remote Sensing Letters 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: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • IEEE Geoscience and Remote Sensing Letters information for authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • IEEE Geoscience and Remote Sensing Letters Institutional Listings
    • Abstract: Presents a listing of institutional institutions relevant for this issue of the publication.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • UV Transient Atmospheric Events Observed Far From Thunderstorms by the
           Vernov Satellite
    • Authors: P. A. Klimov;M. A. Kaznacheeva;B. A. Khrenov;G. K. Garipov;V. V. Bogomolov;M. I. Panasyuk;S. I. Svertilov;R. Cremonini;
      Pages: 1139 - 1143
      Abstract: Usually a thunderstorm region with lightning activity is necessary for the formation of known types of upper atmospheric transient luminous events (TLEs: sprites, emission of light and very low frequency perturbation, blue jets, etc.) with well-recognizable visible emissions. However, some “far-from-thunderstorm” transient events have been detected in some experiments. Measurements of transient atmospheric events (TAEs) were made on board the Vernov satellite by the sensitive UV and IR detector. Remote observation from the satellite’s orbit provided measurements all over the globe and allowed us to study events associated with thunderstorms (lightning, TLEs) and unusual UV flashes (UV TAEs) far from thunderstorm regions. More than 8500 UV TAEs were measured by the Vernov satellite over the globe. Forty seven far-from-thunderstorm TAEs were selected having no lightning discharges during 1 h in a radius of 1000 km around the location of the event according to the Worldwide Lightning Location Network (WWLLN) and Vaisala Global Lightning Data Set (GLD360) data. Special attention was given to six events with complicated temporal structure and low luminosity in the IR channel. Their properties and atmospheric conditions were studied in detail. The analysis of cloud cover in addition to the lightning location networks data demonstrated the low probability of any lightning in the region of measurements.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Sea State Bias of ICESat in the Subarctic Seas
    • Authors: J. Morison;R. Kwok;S. Dickinson;D. Morison;C. Peralta-Ferriz;R. Andersen;
      Pages: 1144 - 1148
      Abstract: The fine spatial resolution of laser altimeters makes them potentially valuable to oceanography studying features at mesoscale, close to land, and in the marginal ice zone. To fulfill this promise, we must understand laser sea state bias (SSB). SSB occurs in the measurement of sea surface height in the presence of waves when the altimeter observations are preferentially influenced by particular parts (e.g., wave troughs) of the wave-covered surface. Radar altimeters have received considerable attention relating radar SSB to wave properties and wind speed. Comparatively, little attention has been devoted to the SSB of laser altimeters, and the studies of laser SSB which have been done have led to indeterminate or ambiguous results even as to sign. Here, we find that to make changes in satellite dynamic ocean topography (DOT) from the Ice, Clouds, and Land Elevation Satellite (ICESat) period, 2004–2009, to the CryoSat-2 period, 2011–2015, consistent with hydrography plus ocean bottom pressure in the subarctic Greenland and Norwegian seas, we need to correct the ICESat DOT for SSB. On average, ICESat SSB is −18% of significant wave height in excess of 1.7 m.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Algorithms for Calculating Topographic Parameters and Their Uncertainties
           in Downward Surface Solar Radiation (DSSR) Estimation
    • Authors: Shengbiao Wu;Jianguang Wen;Dongqin You;Hailong Zhang;Qing Xiao;Qinhuo Liu;
      Pages: 1149 - 1153
      Abstract: Downward surface solar radiation (DSSR) plays an important role in the earth’s surface energy budget. However, it has significant spatial–temporal heterogeneity over the rugged terrain. To accurately capture DSSR, many analytical terrain parameter algorithms based on digital elevation models (DEMs) have been proposed. However, the uncertainties of the DSSR components associated with these algorithms remain unclear. In this letter, we compared three types of terrain parameter algorithms and their respective DSSR component uncertainties at different spatial scales by using 3-D discrete anisotropic radiative model simulations under different atmospheric conditions. The comparison results indicated that differences in slopes, sky view factors, and terrain view factors can be up to 4°, 0.165°, and 0.264°, respectively. For a high atmospheric visibility, the maximum discrepancies of direct solar irradiance and adjacent terrain-reflected irradiance over the high reflective surface (e.g., fresh snow and ice) are 26.7 and $42.8~text {W}cdot text {m}^{2}$ , respectively. In addition, for a low atmospheric visibility, a maximum difference of 31 $text {W}cdot text {m}^{2}$ is identified for diffuse skylight. These uncertainties are nonnegligible when using a high-resolution DEM (e.g., 30 m), but as the DEM resolution becomes coarser, the uncertainties decrease.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Resistivity-Based Temperature Estimation of the Kakkonda Geothermal Field,
           Japan, Using a Neural Network and Neural Kriging
    • Authors: Kazuya Ishitsuka;Toru Mogi;Kotaro Sugano;Yusuke Yamaya;Toshihiro Uchida;Tatsuya Kajiwara;
      Pages: 1154 - 1158
      Abstract: Neural network has been successfully used for field-scale temperature estimation based on resistivity data. Although the methodology is able to estimate temperature distribution based on geophysical data with a limited number of temperature data, it has also been shown that the estimation accuracy can decrease as distance increases from temperature measurements. In this letter, we developed a resistivity-based neural kriging approach to improve the accuracy of estimated temperatures. The neural kriging method in this letter incorporated a variogram of temperature data, which constrained the spatial distribution of temperature. We examined the accuracy of standard neural network approaches, as well as the neural kriging approach, using a cross-validation test. The examination showed that when neural kriging was used, the estimation error and variogram error were reduced by 6%–87% and 191%–285%, respectively. The improvement was also significant at areas distant from well logs. The pattern of estimated temperatures matched the underlying geology and was in accordance with previous studies. The proposed methodology can incorporate other types of geophysical data for estimating other physical parameter distributions.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A Novel Approach for Seismic Time-Frequency Analysis Based on High-Order
           Synchrosqueezing Transform
    • Authors: Wei Liu;Siyuan Cao;Zhiming Wang;Kangkang Jiang;Qingchen Zhang;Yangkang Chen;
      Pages: 1159 - 1163
      Abstract: Time-frequency analysis always plays a central role in the field of seismic processing due to the advantage in characterizing nonstationary signals. In this letter, we present a novel technique for seismic time-frequency analysis based on the high-order synchrosqueezing transform, which obtains more accurate instantaneous frequencies by using the higher order approximations for both amplitude and phase in order to achieve a highly energy-concentrated time-frequency representation. A synthetic example is employed to demonstrate the validity of the proposed method in sharpening time-frequency representation. Application on field data example further proves its potential in enhancing time-frequency resolution and delineating stratigraphic characteristics with higher precision and renders that this technique is promising for seismic data analysis.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • An Iterative Zero-Offset VSP Wavefield Separating Method Based on the
           Error Analysis of SVD Filtering
    • Authors: Xiaokai Wang;Jianyou Chen;Lei Gao;Wenchao Chen;
      Pages: 1164 - 1168
      Abstract: Wavefield separation is one critical step in the vertical seismic profiling (VSP) data processing. In this letter, we present an iterative zero-offset VSP wavefield separating method based on analyzing the error of the singular value decomposition (SVD) low-pass filtering. The error of the SVD low-pass filtering can be divided into the incomplete error and the truncated error. The incomplete error is caused by the incompleteness of subspace, while the truncated error is related to discarding small singular values and corresponding eigenimages. Flattening the strongly correlated component in the 2-D signal can reduce these two errors, and the flattened component can be extracted precisely with the SVD low-pass filtering. The zero-offset VSP data set in seismic data processing records downgoing and upgoing wavefields, which have different propagating directions. By flattening the downgoing and upgoing wavefields alternatively, we propose one iterative zero-offset VSP wavefield separating method. In each iteration, we first flatten the downgoing wavefield to increase its correlation and estimate the downgoing wavefield by using the SVD low-pass filtering, and then, we flatten the upgoing wavefield in the residual to increase its correlation and estimate the upgoing wavefield by using the SVD low-pass filtering. The proposed method is applied to one synthetic data set and one real zero-offset VSP data set. Compared with the commonly used wavefield separation method, our method can have better separation results and reduce the separation error.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Effects of Shadow and Source Overprint on Grounded-Wire Transient
           Electromagnetic Response
    • Authors: Nannan Zhou;Dongyang Hou;Guoqiang Xue;
      Pages: 1169 - 1173
      Abstract: The detection accuracy of the grounded-wire transient electromagnetic method is affected by the incorrect geoelectric structure and burial depth due to shadow and source overprint effects. To better understand these effects, the three conditions under which these effects are present are analyzed separately. First, the analytical expressions are derived for computing the response of the formative wave and the surface wave in homogenous earth. The effects of the formative wave at the anomaly’s location and at the receiving site are analyzed. Then, the response variation due to the presence of an anomaly between the transmitter and the receiver is quantitatively analyzed using the finite-difference method. Finally, the effects of shadow and source overprint on the data inversion are investigated, which is verified by a case study. The wrong geoelectric information is caused by the shadow and source overprint effects. To avoid this, multisource observation can be used to weaken and utilize the shadow and source overprint effects.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Modeling the Effect of Microscopic and Mesoscopic Heterogeneities on
           Frequency-Dependent Attenuation and Seismic Signatures
    • Authors: Yan-Xiao He;Xin-Yu Wu;Kun Fu;Dan Zhou;Shang-Xu Wang;
      Pages: 1174 - 1178
      Abstract: At seismic and sonic frequencies, the major cause of wave attenuation and dispersion in fluid-saturated rocks might be the wave-induced fluid flow on microscopic and mesoscopic scales. However, it is challenging to assess these effects as the attenuation mechanisms related to both heterogeneities that cannot be expected to be independent. This is due to the fact that, fluid flow taking placing at the mesoscopic scale may be impacted by squirt flow mechanism in the presence of microscopic heterogeneities via modifying the dry rock to be frequency-dependent complex moduli. Understanding the integrated effects, related to microscopic squirt flow and wave-induced fluid flow of mesoscopic heterogeneities, would be important for quantifying the relative contribution of the interdependent energy loss mechanisms. We introduce a procedure in this letter to estimate the frequency-dependent seismic attenuation and dispersion by considering the combined presence of microscopic and mesoscopic heterogeneities. The corresponding seismic reflections of a finely stratified model with a dispersive reservoir are calculated using a propagator matrix method in the frequency domain to study the sensitivity of seismic signatures to pore-fluid mobility and rock heterogeneities.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Optimization of RFM’s Structure Based on PSO Algorithm and Figure
           Condition Analysis
    • Authors: Sayyed Hamed Alizadeh Moghaddam;Mehdi Mokhtarzade;Sayyed Ahmad Alizadeh Moghaddam;
      Pages: 1179 - 1183
      Abstract: Rational function model (RFM) faces difficulty in extracting accurate geometric information from remotely sensed images, which is mainly due to the problems of overparameterization and ill-posedness. These problems can be addressed via variable selection methods, in which an optimum subset of rational polynomial coefficients is identified via an optimization algorithm, usually metaheuristic methods [e.g., genetic algorithm and particle swarm optimization (PSO)]. In this letter, we propose a PSO-based method that benefits from a novel cost function. The proposed cost function applies a figure condition analysis, where the sum of estimated errors for the entire image’s pixels is regarded as the cost value. The main advantages of the proposed method, in comparison to its alternatives of the same type, are as follows: 1) in contrast to other metaheuristic-based methods, it can be applied even with a limited number of control points (CPs); 2) since the proposed cost function is a global and continuous one, it yields an appealing RFM from the generalization capability viewpoint (i.e., it leads to satisfying positional accuracies for the entire image, even for those pixels far from CP); and 3) the method is much more stable, which means that it gives very similar results in successive runs. Our experiments, conducted over four real data sets, demonstrate that the proposed method addresses the aforementioned problems, namely, overparameterization and ill-posedness. In addition, it outperforms the typical PSO-based method by 37% on average and also achieves RFM’s structures that are more reliable and more stable than those identified by its alternative.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Semantic Labeling Using a Low-Power Neuromorphic Platform
    • Authors: Jianbin Tang;Benjamin Scott Mashford;Antonio Jimeno Yepes;
      Pages: 1184 - 1188
      Abstract: Deep learning is a powerful technique for the analysis of remote sensing imagery. For applications that require real-time processing on mobile platforms, a low power consumption processing unit is advantageous. The human brain is remarkably powerful at image recognition tasks while operating at very low power consumption levels. Neuromorphic computing designs aim to achieve energy efficiency through the use of spiking neurons and low-precision synapses to perform data processing. We demonstrate here the classification of red, green, blue and depth and hyperspectral data sets using a neuromorphic processing unit (IBM TrueNorth Neurosynaptic System). The convolutional neural-network architecture of the classifier network has been adapted to fit the neuromorphic architecture. The results on overhead imagery and hyperspectral imagery data show that neuromorphic platforms can achieve the state-of-the-art performance in semantic labeling with significantly ( $approx 1000times $ ) lower power consumption than traditional GPU-based solutions.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Animal Lameness Detection With Radar Sensing
    • Authors: Aman Shrestha;Charalampos Loukas;Julien Le Kernec;Francesco Fioranelli;Valentina Busin;Nicholas Jonsson;George King;Martin Tomlinson;Lorenzo Viora;Lance Voute;
      Pages: 1189 - 1193
      Abstract: Lameness is a significant problem for performance horses and farmed animals, with severe impact on animal welfare and treatment costs. Lameness is commonly diagnosed through subjective scoring methods performed by trained veterinary clinicians, but automatic methods using suitable sensors would improve efficiency and reliability. In this letter, we propose the use of radar micro-Doppler signatures for contactless and automatic identification of lameness, and present preliminary results for dairy cows, sheep, and horses. These proof-of-concept results are promising, with classification accuracy above 85% for dairy cows, around 92% for horses, and close to 99% for sheep.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • $l_{1}$+ /+$l_{2}$+ +Norm&rft.title=IEEE+Geoscience+and+Remote+Sensing+Letters&rft.issn=1545-598X&rft.date=2018&rft.volume=15&rft.spage=1194&rft.epage=1198&rft.aulast=Jin;&rft.aufirst=Youlong&rft.au=Youlong+Zou;Ranhong+Xie;Mi+Liu;Jiangfeng+Guo;Guowen+Jin;">Nuclear Magnetic Resonance Spectrum Inversion Based on the Residual Hybrid
           $l_{1}$ / $l_{2}$ Norm
    • Authors: Youlong Zou;Ranhong Xie;Mi Liu;Jiangfeng Guo;Guowen Jin;
      Pages: 1194 - 1198
      Abstract: The residual $l_{2}$ norm is commonly used to measure the fitting error for a nuclear magnetic resonance (NMR) spectrum inversion. However, the solution of the objective function with the residual $l_{2}$ norm is sensitive to noise. When $1 le p < 2$ , the solution of the objective function with the residual $l_{p}$ norm is less sensitive to noise. In this letter, we develop a method for NMR spectrum inversion based on the residual hybrid $l_{1}/l_{2}$ norm, which behaves like $l_{2}$ norm for small residuals and like $l_{1}$ norm for large residuals. The numerical results of the 1-D $T_{2}$ inversion and the 2-D $D$ – $T_{2}$ inversion show that the residual hybrid $l_{1}/l_{2}$ norm method provides an inversion result with a smaller porosity error and exhibits a better ability to distinguish the fluid spectrum peaks.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Building Layout Reconstruction in Concealed Human Target Sensing via UWB
           MIMO Through-Wall Imaging Radar
    • Authors: Yongping Song;Jun Hu;Ning Chu;Tian Jin;Jianwen Zhang;Zhimin Zhou;
      Pages: 1199 - 1203
      Abstract: This letter is devoted to the layout reconstruction via the ultra-wideband (UWB) through-wall imaging radar under one single observation and simultaneously takes account of the real-time human indication. In the proposed framework, layout reconstruction is taken as the preprocessing, where a coherent processing interval consisting of several successive received echoes in the initial stage is first employed to construct a range-Doppler (RD) spectrum. Then, in the RD spectrum, a series of selected discrete Doppler frequency signals is used to form Doppler back projection (BP) images. Finally, in the Doppler BP image stack, we design a 3-D constant false alarm rate detector to extract the building layout. Once completed, the achieved layout as auxiliary information is fused with the simultaneous human indication. Through-wall experiments show that the proposed method can effectively extract the covered layout of multiple walls under one single view and accordingly provide strong support for the concealed human sensing.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Millimeter-Wave Ultrahigh Resolution SAR Image Classification Based on a
           New Feature Set
    • Authors: Wenjin Wu;Xinwu Li;Huadong Guo;Lei Liang;
      Pages: 1204 - 1208
      Abstract: Aiming at the problems and prospects in millimeter-wave ultrahigh resolution synthetic aperture radar applications, we have developed a method with a new feature set for sophisticated classification of large images. It includes innovative parameters derived from different kinds of spectral and characteristic signatures, such as the correlation signature, radial spectrum, and angular spectrum. These features can mine repetitive information from the fragmented patterns and enhance the texture description in different aspects. In the experiment, the proposed feature set achieves 89% overall accuracy which is 25% higher compared with the gray-level co-occurrence matrix feature set. The four new features contribute to over 50% of the accuracy improvement with a significant increase of the accuracy for vehicles and show a fair performance for all the categories.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • (L + S)-RT-CCD for Terrain Paths Monitoring
    • Authors: Filippo Biondi;
      Pages: 1209 - 1213
      Abstract: In past research, the problem of maritime targets detection and motion parameter estimation has been tackled. This letter aims to contribute to preventing illegal immigration, solving the problem of obtaining reliable paths detection of targets in terms of temporal decorrelation observed on coherent change detection (CCD) maps occurring between two or more complex-valued synthetic aperture radar (SAR) images. Most detection problems are related to terrain clutter and platform motion instabilities which make the paths structure detection and reconnaissance difficult. This letter presents a complete procedure called low-rank plus sparse decomposition radon transform (RT) CCD for automatic estimation and tracking of target paths by evaluating the generated temporal decorrelation CCD-SAR images, observed at the desert environments. The algorithm consists of evaluating a dual-stage low-rank plus sparse decomposition (LRSD) assisted by RT for clutter reduction, sparse object detection, and precise path inclination estimation. The algorithm is based on the robust principal component analysis (RPCA) implemented by convex programming. The LRSD algorithm permits the extrapolation of sparse objects of interest consisting of the incoherent patterns generated by targets from the unchanging low-rank and more coherent background. This dual-stage RPCA and RT applied to SAR-CCD surveillance permits fast detection and enhanced parameter estimation of terrain target paths.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A Volume Optimization Method to Improve the Three-Stage Inversion
           Algorithm for Forest Height Estimation Using PolInSAR Data
    • Authors: Tayebe Managhebi;Yasser Maghsoudi;Mohammad Javad Valadan Zoej;
      Pages: 1214 - 1218
      Abstract: This letter proposes a novel method to improve the results of the three-stage inversion algorithm, using polarimetric synthetic aperture radar interferometry. Since the accuracy of the estimated forest height is affected by the volume only coherence selection, finding the optimum coherence value is an important challenge for the conventional three-stage method. In the three-stage algorithm, a specific polarization state, HV, is usually used as the volume only channel. However, in this letter, an optimization algorithm is developed to find a more accurate volume only coherence on the coherence line. We used the experimental airborne SAR L-band single-baseline single-frequency polarimetric interferometry data to evaluate the proposed algorithm. The experimental results show the proposed optimized volume only coherence leads to 2.9-m improvement in the results of the three-stage inversion algorithm.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A Multibaseline InSAR Phase Unwrapping Method Using Designed Optimal
           Baselines Obtained by Motion Compensation Algorithm
    • Authors: Ning Cao;Hanwen Yu;Hyongki Lee;
      Pages: 1219 - 1223
      Abstract: Multibaseline (MB) interferometric synthetic aperture radar phase unwrapping (PU) has the advantage of using the baseline diversity to increase the ambiguity intervals of the interferometric phases and overcome the limitation of the phase continuity assumption. One major challenge of the MB PU is that the performance of PU greatly depends on the baseline configuration. In this letter, the motion compensation algorithm is used to modify the baselines of the interferograms. Therefore, optimal baseline configuration can be used in the MB PU to improve the PU performance. Real ALOS/PALSAR data over Himalayan mountain area have been used to verify the performance of the proposed method. The experimental results show that the MB PU with modified baselines becomes more reliable.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Investigation of Branching Conditions in Model-Based Decomposition Methods
    • Authors: Himanshu Maurya;Rajib Kumar Panigrahi;
      Pages: 1224 - 1228
      Abstract: In this letter, we investigate the existing branching conditions used to solve the unknown model-coefficients of model-based decomposition methods and show that they are less efficient in discriminating between dominant surface and dihedral scattering mechanisms. The discrimination ability of the branching conditions further deteriorates when the target has some random slope and orientation. This greatly suppressed the performance of the model-based decomposition methods. To overcome this problem, we propose an efficient alternate to existing branching conditions of model-based methods. The proposed branching condition is based on the value of the alpha ( $alpha $ ) angle derived from the eigenvector analysis of the measured coherency matrix. The roll-invariance property of $alpha $ angle makes it work efficiently even in the sloped and oriented areas. The proposed concept is experimentally validated over three different polarimetric synthetic aperture radar (PolSAR) data sets. The effectiveness of the $alpha $ angle is analyzed and compared with the other branching conditions in terms of ability to discriminate between dominant surface and dihedral scattering mechanisms. The experimental results on different PolSAR data sets clearly demonstrate that by replacing the existing branching conditions with the $alpha $ angle, the performances of the model-based decomposition methods are significantly improved.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A Modified General Polarimetric Model-Based Decomposition Method With the
           Simplified Neumann Volume Scattering Model
    • Authors: Qinghua Xie;Jianjun Zhu;Juan M. Lopez-Sanchez;Changcheng Wang;Haiqiang Fu;
      Pages: 1229 - 1233
      Abstract: This letter proposes a modified general polarimetric model-based decomposition method which includes a simplified Neumann volume scattering model (SNVSM). This is useful to avoid a known limitation in one of the state-of-the-art general model-based decomposition methods (i.e., Chen’s method), which considers only four possible discrete volume scattering models. Two types of SNVSM, assuming horizontal or vertical dipoles, are derived from the Neumann volume scattering model. The resulting volume coherency matrix exhibits a continuous range of volume scattering models. In addition, this volume model covers both random and nonrandom volume cases, which are distinguished by a randomness parameter. Monte Carlo simulations are used to test this approach. The proposed method with SNVSM overall improves the final accuracy of estimated parameters in comparison with the original approach and shows consistency with another existing generalized volume scattering model (GVSM). In addition, results from two fully polarimetric C- and L-band AIRSAR images over San Francisco region show that the proposed method produces reasonably physical results and outperforms the traditional Y4R method. Finally, the differences obtained between SNVSM and GVSM in two building areas show the potential advantage of SNVSM in identifying more types of volume scenes than that of GVSM.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Isotropization of Quaternion-Neural-Network-Based PolSAR Adaptive Land
           Classification in Poincare-Sphere Parameter Space
    • Authors: Kazutaka Kinugawa;Fang Shang;Naoto Usami;Akira Hirose;
      Pages: 1234 - 1238
      Abstract: Quaternion neural networks (QNNs) achieve high accuracy in polarimetric synthetic aperture radar classification for various observation data by working in Poincare-sphere-parameter space. The high performance arises from the good generalization characteristics realized by a QNN as 3-D rotation as well as amplification/attenuation, which is in good consistency with the isotropy in the polarization-state representation it deals with. However, there are still two anisotropic factors so far which lead to a classification capability degraded from its ideal performance. In this letter, we propose an isotropic variation vector and an isotropic activation function to improve the classification ability. Experiments demonstrate the enhancement of the QNN ability.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Decomposition-Feature-Iterative-Clustering-Based Superpixel Segmentation
           for PolSAR Image Classification
    • Authors: Biao Hou;Chen Yang;Bo Ren;Licheng Jiao;
      Pages: 1239 - 1243
      Abstract: Compared with traditional pixel-based polarimetric synthetic aperture radar (PolSAR) image classification methods, superpixel-based methods take advantages of the spatial information of pixels, so they can overcome the influence of speckle noise on the classification result. Since traditional superpixel methods do not utilize the scattering characteristics of a PolSAR image, the boundaries of the superpixels are poorly preserved. The inaccuracy of superpixel segmentation boundaries has a negative impact on the subsequent classification. In this letter, we propose a decomposition-feature-iterative-clustering (DFIC) superpixel segmentation method for PolSAR images. The DFIC method innovatively introduces the decomposition features in generating superpixels, so the superpixel segmentation boundaries are well preserved. Because we selectively utilize superpixel information to classify the PolSAR images by setting a threshold, the effect of superpixel segmentation inaccuracy on the classification results is reduced. Experiments on two real PolSAR images demonstrate that the proposed method outperforms several state-of-the-art superpixel methods, and that the DFIC superpixel-based classification obtains better results than the other pixel-based methods.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • An Autofocus Cartesian Factorized Backprojection Algorithm for Spotlight
           Synthetic Aperture Radar Imaging
    • Authors: Yin Luo;Fengjun Zhao;Ning Li;Heng Zhang;
      Pages: 1244 - 1248
      Abstract: A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP has not been discussed. In this letter, an autofocus CFBP algorithm is proposed to compatibly combine the autofocus processing within the CFBP. After modifying the spectrum compression step in the CFBP, the approximate Fourier transformation (FT) relationship between the modified compensated subaperture images and the corresponding range-compressed phase history data in the Cartesian coordinate is revealed. The phase error is obtained by the multiple aperture map drift method, and the singular value decomposition total least square method is combined to improve the estimate robustness. Employing the range blocking method, the range variance of the phase error is compensated. The proposed algorithm inherits the advantages of the CFBP. Experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the proposed approaches.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A Parameterized Multiangular Microwave Emission Model of L-, C-, and
           X-Bands for Corn Considering Multiple-Scattering Effects
    • Authors: Linna Chai;Qian Zhang;Jiancheng Shi;Shaomin Liu;Shaojie Zhao;Haiying Jiang;
      Pages: 1249 - 1253
      Abstract: The matrix doubling (MD) model is a numerical solution to the radiative transfer equation. It can achieve better accuracy in simulating microwave signals from vegetated terrain by considering multiple-scattering effects. However, it is difficult to apply the MD model to retrieving work due to its high complexity. This letter presents a case study performed on corn to demonstrate a multiangular (5°–65°), multiband (1.4/6.925/10.65 GHz) microwave emission model considering multiple-scattering effects by parameterizing the MD model. The simulated emissivity differences between the theoretical model and parameterized model are small. The mean absolute percent errors are all less than 1%, and the root mean square errors (RMSEs) are all within the range of $10^{-3}$ . Validations using airborne polarimetric L-band microwave radiometer data and ground-based trunk-mounted multifrequency microwave radiometer data indicate that the parameterized model achieves good accuracy with overall RMSEs within 8K at all three bands.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Deformable Convolutional Neural Networks for Hyperspectral Image
           Classification
    • Authors: Jian Zhu;Leyuan Fang;Pedram Ghamisi;
      Pages: 1254 - 1258
      Abstract: Convolutional neural networks (CNNs) have recently been demonstrated to be a powerful tool for hyperspectral image (HSI) classification, since they adopt deep convolutional layers whose kernels can effectively extract high-level spatial–spectral features. However, sampling locations of traditional convolutional kernels are fixed and cannot be changed according to complex spatial structures in HSIs. In addition, the typical pooling layers (e.g., average or maximum operations) in CNNs are also fixed and cannot be learned for feature downsampling in an adaptive manner. In this letter, a novel deformable CNN-based HSI classification method is proposed, which is called deformable HSI classification networks (DHCNet). The proposed network, DHCNet, introduces the deformable convolutional sampling locations, whose size and shape can be adaptively adjusted according to HSIs’ complex spatial contexts. Specifically, to create the deformable sampling locations, 2-D offsets are first calculated for each pixel of input images. The sampling locations of each pixel with calculated offsets can cover the locations of other neighboring pixels with similar characteristics. With the deformable sampling locations, deformable feature images are then created by compressing neighboring similar structural information of each pixel into fixed grids. Therefore, applying the regular convolutions on the deformable feature images can reflect complex structures more effectively. Moreover, instead of adopting the pooling layers, the strided convolution is further introduced on the feature images, which can be learned for feature downsampling according to spatial contexts. Experimental results on two real HSI data sets demonstrate that DHCNet can obtain better classification performance than can several well-known classification methods.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Morphological Band Selection for Hyperspectral Imagery
    • Authors: Jingyu Wang;Xianyu Wang;Ke Zhang;Kurosh Madani;Christophe Sabourin;
      Pages: 1259 - 1263
      Abstract: In this letter, a novel morphological band selection method is proposed to obtain the most representative bands from hyperspectral image (HSI) in an unsupervised manner. In order to sufficiently process the HSI, we propose to use only a small set of data instead of using the original full data. For the obtained clusters, the differences of spectral response curves are applied to measure the local discrimination capability of bands, of which the local maximum value point is yielded based on the morphological processing. To verify the performance of the proposed method, the robustness of the parameters has been evaluated, while the effectiveness and superiority have been tested on three popular hyperspectral data sets. The experiment results have shown that the proposed method outperforms other methods.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • An Automatic Dictionary Construction Framework for Sparsity-Based
           Hyperspectral Target Detectors
    • Authors: Mehmet Altan Toksöz;Kemal Gürkan Toker;Gonca Gül Güngör;
      Pages: 1264 - 1268
      Abstract: The mechanisms behind the sparsity-based techniques for hyperspectral target detection and classifications applications are quite similar except the construction methods of the dictionaries used by the algorithms. In hyperspectral image classification, the dictionaries are formed using some known labeled training samples for each class. In contrast, the only a priori target spectrum information is available for the target detection applications. In addition, most of the time, the background materials are unknown in an arbitrary scene. Although some practical approaches such as sliding window exist, their performances are highly unsatisfactory. In order to increase the detection performance of the sparsity-based methods, we propose an automatic dictionary construction framework which is based on a couple of stages consisting of dimension reduction, k-means clustering, connected component labeling via spatial techniques, and spectral methods such as constrained energy minimization filtering. Our experiments show that the proposed approach outperforms the conventional methods, and it is a promising framework for hyperspectral target detection applications.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A New Variational Model in Texture Space for Pansharpening
    • Authors: Meysam Lotfi;Hassan Ghassemian;
      Pages: 1269 - 1273
      Abstract: In this letter, a new variational model in texture space for pansharpening is proposed to increase spatial information of multispectral image, while preserving spectral and spatial consistencies of pansharpened image. Geometric structure consistency between panchromatic image and pansharpened image is very important for preserving spatial characteristics, especially in borders, edges, and textured regions. G-norm can extract more pure and accurate texture, curvature, and oscillating details than do previous first-order and second-order-based methods, such as Gradient and Hessian operators. Therefore, we aim to use the G-space to maintain spatial information. Experimental results show that the proposed method has a better performance in terms of both spatial and spectral qualities; however, it is not efficient in terms of computation time.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A Segmentation Method for High Spatial Resolution Remote Sensing Images
           Based on the Fusion of Multifeatures
    • Authors: Dawei Liu;Ling Han;Xiaohong Ning;Yongzhong Zhu;
      Pages: 1274 - 1278
      Abstract: A novel method based on the fusion of spectral, texture, and shape features is proposed for the segmentation of high spatial resolution remote sensing images. The method uses the region merging idea to get the final segmentation result on the basis of initial segmentation. Texture features of the regions are obtained by the nonsubsampled contourlet transform. An integrated region merging criterion is built by combining the texture, spectral, and shape features. To ensure the efficiency of the method, the region adjacency graph and the nearest neighbor graph are used to maintain the adjacency relations in the region merging stage. The global optimization strategy is adopted to realize the image segmentation gradually. Two experiments are performed to verify the effectiveness of the proposed method. One experiment validates the influence of the merging criteria with different feature combinations on the segmentation results. Another experiment compares the effect of the method with the segmentation methods embedded in ENVI and eCognition. Experimental results show that the method can make full use of the features of the images to achieve accurate and efficient segmentations.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime
           Light Composite Data
    • Authors: Bailang Yu;Min Tang;Qiusheng Wu;Chengshu Yang;Shunqiang Deng;Kaifang Shi;Chen Peng;Jianping Wu;Zuoqi Chen;
      Pages: 1279 - 1283
      Abstract: Accurate information on urban areas at regional and global scales is required for various socioeconomic and environmental applications. The nighttime light (NTL) composite data have proven to be an effective data source for extracting urban areas. Various urban mapping methods have been proposed in the literature to extract urban built-up areas from the Defense Meteorological Satellite Program’s Operational Linescan System NTL data with a variable accuracy. However, most of the previous methods cannot be directly applied to the NTL data derived from the Suomi National Polar-orbiting Partnership Satellite with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor onboard. In this letter, we introduced a logarithmic transformation to preprocess the NPP-VIIRS NTL composite data. Then, four popular methods for urban built-up area extraction were tested using the original and log-transformed NTL data, respectively. The selected methods included the thresholding technique, Sobel-based edge detection, neighborhood statistics analysis, and watershed segmentation. The accuracy of the results was evaluated through validating the urban areas derived using each method against the referenced urban areas obtained from the National Land Cover Database for the U.S.. The results indicated that logarithmic transformation is an effective procedure for enhancing the difference between urban built-up areas and nonurban areas. The selected methods for urban built-up area extraction were found to perform better on the log-transformed NTL data than the original NTL data.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Analysis of Airborne LiDAR Point Clouds With Spectral Graph Filtering
    • Authors: Eda Bayram;Pascal Frossard;Elif Vural;Aydın Alatan;
      Pages: 1284 - 1288
      Abstract: Separation of ground and nonground measurements is an essential task in the analysis of light detection and ranging (LiDAR) point clouds; however, it is challenge to implement a LiDAR filtering algorithm that integrates the mathematical definition of various landforms. In this letter, we propose a novel LiDAR filtering algorithm that adapts to the irregular structure and 3-D geometry of LiDAR point clouds. We exploit weighted graph representations to analyze the 3-D point cloud on its original domain. Then, we consider airborne LiDAR data as an irregular elevation signal residing on graph vertices. Based on a spectral graph approach, we introduce a new filtering algorithm that distinguishes ground and nonground points in terms of their spectral characteristics. Our complete filtering framework consists of outlier removal, iterative graph signal filtering, and erosion steps. Experimental results indicate that the proposed framework achieves a good accuracy on the scenes with data gaps and classifies the nonground points on bridges and complex shapes satisfactorily, while those are usually not handled well by the state-of-the-art filtering methods.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • A New Geomagnetic Matching Navigation Method Based on Multidimensional
           Vector Elements of Earth’s Magnetic Field
    • Authors: Zhuo Chen;Qi Zhang;Mengchun Pan;Dixiang Chen;Chengbiao Wan;Fenghe Wu;Yang Liu;
      Pages: 1289 - 1293
      Abstract: At present, most of the geomagnetic navigation methods are based on the single geomagnetic scalar characteristics, and the iterative closest contour point (ICCP) algorithm is the most extensively utilized. But when there are several contour lines with the same scalar value in the matching area, or the scalar feature in this area is not obvious, navigation accuracy will be seriously affected. In this letter, a new geomagnetic navigation method based on vector matching algorithm [vector ICCP (VICCP)] is proposed, combining the searching principle of trusted points sets and tracks with the matching principle of geomagnetic vector correlation restriction. Consequently, navigation results of it will have greater accuracy, more reliable validity, and practicability compared with the traditional ICCP algorithm. The performance of the matching and the correction methods is analyzed by simulation and experiment. In simulation, the position error of VICCP is less than ICCP under the conditions of nonobvious scalar geomagnetic features, which are, respectively, reduced from 1340.0 to 72.8 m, from 1267.7 to 33.3 m, and from 14115.7 to 36.9 m. And the conclusion is also verified in the experiment. In addition, VICCP algorithm is not sensitive to initial position. Thus, the proposed VICCP algorithm can effectively improve the performance of geomagnetic navigation.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • None-Asphericity-Error Method for Magnetic Dipole Target Detection
    • Authors: H. H. Jin;Z. H. Zhuang;H. B. Wang;
      Pages: 1294 - 1298
      Abstract: There is always a relative motion between the magnetometer and the magnetic target. Especially when both are moving, in certain scenarios, the magnetometer is demanded to have the capability of point-by-point and real-time positioning. In this letter, we deduce normalized gradient formulas based on the total field gradient (TFG) and the tensor module gradient (TMG) of the target, respectively. In addition, we analyze the capability of these two formulas to determine the direction of the target. Afterward, the none-asphericity-error method based on the union of aforementioned formulas (TFG-TMG) is proposed. Simulation results show that the union method has no asphericity error on magnetic target localization as well as evaluating the magnetic moment of the target. Even in some noisy environment, the direction error is smaller than 1° and the mean error of the magnetic moment is smaller than 5% of the magnetic moment of the target.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Spatio-Temporal Segmentation Applied to Optical Remote Sensing Image Time
           Series
    • Authors: Wanderson Santos Costa;Leila Maria Garcia Fonseca;Thales Sehn Körting;Hugo do Nascimento Bendini;Ricardo Cartaxo Modesto de Souza;
      Pages: 1299 - 1303
      Abstract: The availability of a large amount of remote sensing data made Earth Observation increasingly accessible and detailed. High temporal and spatial resolution sensors are responsible for making available data sets of time series in unprecedented proportions. Within this context, the use of efficient segmentation algorithms of remote sensing imagery represents an important role in this scenario, because they provide homogeneous regions in space-time and hence simplify the data set. In addition, the spatio-temporal segmentation can bring a new way of interpreting data by means of analyzing contiguous regions in time. This letter describes a method for image segmentation applied to time series of the Earth Observation data. We adapted the traditional region growing method to detect homogeneous regions in space and time. Study cases were conducted by considering the dynamic time warping algorithm as the homogeneity criterion to grow regions. Tests on high temporal resolution image sequences from Moderate Resolution Imaging Spectroradiometer and Landsat-8 Operational Land Imager vegetation indices and comparisons with other distance measurements provided satisfactory outcomes.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
  • Introducing IEEE Collabratec
    • Pages: 1304 - 1304
      Abstract: Advertisement, IEEE.
      PubDate: Aug. 2018
      Issue No: Vol. 15, No. 8 (2018)
       
 
 
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