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  Subjects -> ELECTRONICS (Total: 158 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 3)
Advances in Electronics     Open Access   (Followers: 11)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 6)
Advances in Microelectronic Engineering     Open Access   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 12)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 135)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 17)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 10)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 6)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 19)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 15)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 23)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 8)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 27)
China Communications     Full-text available via subscription   (Followers: 6)
Circuits and Systems     Open Access   (Followers: 10)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 52)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 32)
Electronic Markets     Hybrid Journal   (Followers: 6)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 16)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics For You     Partially Free   (Followers: 7)
Electronics Letters     Hybrid Journal   (Followers: 20)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 31)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 2)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 9)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 5)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal  
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 43)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 2)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 35)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 31)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 22)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 6)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 27)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 26)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 43)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 11)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 21)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 10)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 15)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 39)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 5)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 10)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 10)
IET Power Electronics     Hybrid Journal   (Followers: 19)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 13)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 6)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 20)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Informatik-Spektrum     Hybrid Journal   (Followers: 1)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 6)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 15)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 4)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 13)
International Journal of Antennas and Propagation     Open Access   (Followers: 7)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 1)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 1)
International Journal of Electronics & Data Communication     Open Access   (Followers: 6)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 7)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 6)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 2)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 5)
International Journal of Nanoscience     Hybrid Journal   (Followers: 2)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 11)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 6)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 3)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 3)
International Journal on Communication     Full-text available via subscription   (Followers: 12)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 7)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 1)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 6)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 9)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 4)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 2)
Journal of Electronics (China)     Hybrid Journal   (Followers: 3)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 106)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 5)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 7)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 1)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 7)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 6)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Sensors     Open Access   (Followers: 15)
Journal of Signal and Information Processing     Open Access   (Followers: 7)
Jurnal Infotel     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 5)
Metrology and Measurement Systems     Open Access   (Followers: 3)
Microelectronics and Solid State Electronics     Open Access   (Followers: 12)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 30)
Nanotechnology, Science and Applications     Open Access   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Open Journal of Antennas and Propagation     Open Access   (Followers: 2)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 7)
Paladyn, Journal of Behavioral Robotics     Open Access  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 4)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 5)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 29)
Semiconductors and Semimetals     Full-text available via subscription  
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 1)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 34)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 8)
Solid-State Electronics     Hybrid Journal   (Followers: 5)
Superconductor Science and Technology     Hybrid Journal   (Followers: 1)
Synthesis Lectures on Power Electronics     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 4)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 4)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 3)
Wireless Power Transfer     Full-text available via subscription   (Followers: 2)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 9)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.632]   [H-I: 19]   [29 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • Information for Authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Institutional Listings
    • Abstract: The IEEE GRSS Society is grateful for the support given by the organizations listed and invites applications for Institutional Listings from other firms interested in the field of geoscience and remote sensing.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Front cover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Table of Contents
    • Pages: 2097 - 2100
      Abstract: Presents the table of contents for this issue of the publication.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Foreword to the Special Issue on the 2015 IEEE International Geoscience
           and Remote Sensing Symposium
    • Pages: 2101 - 2103
      Abstract: The papers in this special section were presented at the 2015 International Geoscience and Remote Sensing Symposium (IGARSS 2015)was held on July 26 through 31, 2015 at the Milano Congressi Center in Milano, Italy. The main theme of the conference was Remote Sensing: Understanding the Earth for a Safer World.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Efficient and Robust RFI Extraction Via Sparse Recovery
    • Pages: 2104 - 2117
      Abstract: This paper presents a simple adaptive framework for robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB) radar signals in challenging bandwidth management environments. RFI sources pose critical challenges for UWB systems since 1) RFI often occupies a wide range of the radar’s operating frequency spectrum; 2) RFI might have significant power; and 3) RFI signals are difficult to predict and model due to the nonstationary nature as well as the complexity of various communication devices. Our proposed framework involves an initial RFI estimation step that operates directly on already contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital prior information is then taken into account to construct an adaptive RFI dictionary with various sinusoidal patterns covering the aforementioned RFI-contaminated frequency spectrum. Finally, we employ a sparsity-driven optimization strategy to separate and then extract RFI from the received radar signals. Our method can be implemented as a denoising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks such as target detection and classification. Recovery results from extensive simulated data sets as well as real-world signals collected by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • SAR Image Despeckling by Soft Classification
    • Pages: 2118 - 2130
      Abstract: We propose a new approach to synthetic aperture radar (SAR) despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixelwise characterization of the image, one can take advantage of this diversity by selecting the most appropriate combination of estimators for each image region. Following this paradigm, we develop a simple algorithm where only two state-of-the-art despeckling tools, characterized by complementary properties, are linearly combined. To ensure the smooth combination of contributes, thus avoiding new artifacts, we propose a novel soft classification method, where a basic estimate of homogeneity is improved through nonlocal and multiresolution processing steps. The results of a number of experiments conducted on both synthetic and real-world SAR images are very promising, thus confirming the potential of the proposed approach.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Scattering-Based SARBM3D
    • Pages: 2131 - 2144
      Abstract: Interpreting synthetic aperture radar (SAR) images may be a very challenging task, even for expert users. One of the main reasons is the multiplicative speckle noise typical of coherent acquisition systems. Therefore, despeckling can be expected to play a key role in the full exploitation of SAR imagery potential. However, even state-of-the-art despeckling algorithms neglect the physical phenomena hidden behind SAR imagery. Image acquisition depends on electromagnetic scattering, which is also at the basis of speckle noise. Taking into account scattering issues into more physical-based despeckling algorithms may only benefit the overall performance. In this paper, we propose a scattering-based (SB) version of the SAR block-matching 3D (BM3D) filter, named SB-SARBM3D. SARBM3D can be arguably considered as one of the most promising and accurate despeckling algorithms, providing a good compromise between speckle reduction and detail preservation. We modify the original algorithm so as to exploit the prior information available on the imaged scene, taken into account based on scattering concepts. The new algorithm is tested in a variety of different and complementary simulated scenarios, and its performance is assessed objectively by means of numerous synthetic parameters. Moreover, comparison with different state-of-the-art despeckling algorithms is performed on some actual SAR images, both inherent to natural and urbanized areas, for subjective evaluation. Thanks to the prior information, SB-SARBM3D outperforms the original algorithm in terms of both speckle reduction and detail preservation. Moreover, it reduces the annoying artifacts introduced sometimes by SARBM3D in homogeneous areas of the image.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • An Improved Adaptive Regularization Method for Forward Looking Azimuth
           Super-Resolution of a Dual-Frequency Polarized Scatterometer
    • Pages: 2145 - 2159
      Abstract: Dual-frequency polarized scatterometer (DFPSCAT) is a pencil-beam rotating scatterometer which is designed for snow water equivalent (SWE) measurement, and Doppler beam sharpening (DBS) technique is proposed for DFPSCAT to achieve the azimuth resolution. However, the DBS technique is inapplicable for the forward-looking and afterward-looking regions. Based on an approximate aperiodic model of scatterometer echo signal, an improved adaptive regularization deconvolution algorithm with gradient histogram preservation (GHP) constraint is implemented to settle the problem. To investigate its performance of resolution enhancement and resulted accuracy, both a synthetic backscattering coefficient ( $sigma^{0}$ ) field reconstruction and SWE $sigma^{0}$ reconstruction are carried out. The results show that the proposed method can recover the truth signal and achieve azimuth resolution of 2 km with the designed scatterometer system, which is required by the SWE retrieval. Moreover, the relative errors of reconstructed $sigma^{0}$ are less than 0.5 dB that satisfy the accuracy requirement for SWE retrieval, and comparisons with observed results show that the error reduction is more than 0.03 dB. Meanwhile, a comparison between the proposed algorithm and some existing resolution enhancement methods is analyzed, which concludes that the proposed method can obtain a comparable resolution enhancement as $L_{1}$ method and has less noise. The technique is also verified with advanced scatterometer (ASCAT) scatterometer data.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Ground-Based SAR Wide View Angle Full-Field Imaging Algorithm Based on
           Keystone Formatting
    • Pages: 2160 - 2170
      Abstract: The interest of applying ground-based synthetic aperture radar (GB-SAR) in terrain displacement monitoring has increased for decades. Due to the large illuminating coverage and suitable working bandwidth, GB-SAR can achieve multitemporal surface deformation maps of the entire terrain with high spatial resolution and submilimetric accuracy. Unfortunately, focusing GB-SAR data in an efficient way is accompanied with a series of complicated engineering problems and should be solved. In this paper, a novel imaging algorithm tailored for GB-SAR data is proposed. First, the main characteristics of GB-SAR data are analyzed, and a signal model is then built up. Second, corresponding to this model, an imaging algorithm is put forward, which is based on keystone formatting and range-Doppler domain blocking. This algorithm is phase-preserving and can be widely applied in near-field, far-field, and wide view angle scenarios. Moreover, its computational cost is quite low, which includes only one time of linear interpolation and several times of fast Fourier transforms and complex multiplications. Finally, the algorithm is extensively validated both with numerical simulations and real GB-SAR data.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Experimental Study of Ionospheric Impacts on Geosynchronous SAR Using GPS
           Signals
    • Pages: 2171 - 2183
      Abstract: The L-band geosynchronous synthetic aperture radar (GEO SAR) is very susceptible to ionosphere as the significant increases of its integration time and wide swath, leading to image drifts and degradations. This paper demonstrates an experimental study of analyzing ionospheric impacts on GEO SAR, including both background ionosphere and ionospheric scintillation. The experiment consists of two parts. One is the global positioning system (GPS) data recording in which we employ GPS satellites to probe ionosphere and collect the transionosphere GPS signals. Then the recorded signals are used to create the data basis on which simulations are based. The other is the reconstruction of the signal distortions based on the GPS data. Then the two parts are combined to generate the ionosphere-impacted GEO SAR signals. But GEO SAR has very different orbit trajectories from GPS. Thus, in the real operation, the transformation of the temporal–spatial frame between GPS and GEO SAR should be first performed before the focusing and the evaluation are carried out. In cases of current GEO SAR configurations, the background ionosphere will induce image drifts but can be corrected through image registration techniques. The image is also likely to get defocused in azimuth when the second and higher derivatives of total electron content exceed thresholds which are dependent on GEO SAR configurations and the corresponding integration time. Comparatively, scintillations will mainly affect the focusing in azimuth, especially for integrated sidelobe ratios (ISLRs). But scintillations rarely occur over China mainland, and it is suggested to avoid the GEO SAR working during its occurrence.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Superresolution Downward-Looking Linear Array Three-Dimensional SAR
           Imaging Based on Two-Dimensional Compressive Sensing
    • Pages: 2184 - 2196
      Abstract: For downward-looking linear array 3-D synthetic aperture radar (SAR), the azimuth and cross-track resolution are unacceptable due to the length limitation of synthetic aperture and linear array. Hence, superresolution reconstruction algorithms are desired. Since the signal to be reconstructed is sparse on the 2-D azimuth–cross-track plane, it is quite suitable to apply the compressive sensing theory to obtain the images. The existed imaging algorithms for downward-looking linear array 3-D SAR are based on 1-D compressive sensing, which could bring the couple effect between different directions. To solve this problem, a novel 3-D imaging algorithm based on 2-D compressive sensing is proposed in this paper. Instead of converting the sparse reconstruction of 2-D matrix signals to the sparse reconstruction of 1-D vectors, the proposed algorithm directly reconstructs the 2-D sparse signals on overcomplete dictionaries with separable atoms. It not only provides superresolution performance, but also reduces the storage of data acquisition and processing. Furthermore, a definition of joint sparse sampling strategy is given to reconstruct the measurement matrices for further improving the computational efficiency of the imaging algorithm. Moreover, in order to investigate the limits of the proposed algorithm, the theory analysis of Cram $acute{e}$ r–Rao bound is derived and compared with the standard deviation. Finally, numerical simulations under the noise scenarios and the principle prototype experiment on real data are shown to demonstrate the validity and the limits of the proposed algorithm.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Nonsmooth Nonconvex Optimization for Low-Frequency Geosounding Inversion
    • Pages: 2197 - 2205
      Abstract: A study of the application of nonconvex regularization operators to the electromagnetic sounding inverse problem is presented. A comparison is presented among three nonconvex regularization algorithms: one smooth usually considered, two nonsmooth, and a convex one, the total variation (TV) operator. One of the nonsmooth nonconvex regularization methods is a novel implementation based on the Legendre–Fenchel transform and the Bregman iterative algorithm. The nonconvex regularization operator is approximated by the convex dual, and the minimization is then implemented considering the equivalence between the Bregman iteration and the augmented Lagrangian methods. The algorithm is simple and provides for better models when applied to synthetic data, than those obtained with TV, and other nonconvex smooth regularizers. Results of the application to field data are also presented, observing that NS2 recovers a model in better agreement with the truth, compared to those obtained with additional magnetometric resistivity data by other researchers.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • SAR Imagery Feature Extraction Using 2DPCA-Based Two-Dimensional
           Neighborhood Virtual Points Discriminant Embedding
    • Pages: 2206 - 2214
      Abstract: Synthetic aperture radar (SAR) is an important microwave sensor that is capable of high-resolution imaging. Extracting valuable features from the SAR target imagery is one of crucial issues in SAR automatic target recognition (ATR). In this paper, we propose a new feature extraction method named 2-D principal-component-analysis-based 2-D neighborhood virtual points discriminant embedding (2DPCA-based 2DNVPDE) for SAR ATR. The SAR imagery is projected into the feature space by 2DPCA and 2DNVPDE in this approach. 2DPCA is able to preserve the global spatial structure of the original imagery, while 2DNVPDE establishes the spatial relationships of the neighborhoods to find the classification information from the neighborhoods of the samples. Hence, our method can extract powerful recognition information and represent the original image in low dimensions. Based on the MSTAR dataset, the experimental results show that the proposed method is able to achieve a higher recognition rate with a lower feature dimension over some existing SAR imagery feature extraction methods. Besides, it indicates that our method has a significant advantage in recognition performance and a lower sensitivity in statistical standpoint.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Land Cover Semantic Annotation Derived from High-Resolution SAR Images
    • Pages: 2215 - 2232
      Abstract: Users of remote sensing images analyzing land cover characteristics are very much interested in classification schemes that define a consistent set of target categories. Up to now, a number of established classification schemes are mainly being used by interpreters of medium-resolution optical satellite images focusing on large-scale land cover. In contrast, we concentrate in this publication on the definition of a new classification scheme for high-resolution synthetic aperture radar (SAR) images that are mostly taken over built-up areas. Here, we can see many small details of buildings, industrial facilities, and infrastructure that have to be classified. However, the appearance of details in high-resolution SAR images is often difficult to understand for human observers, and, therefore, calls for an automated semantic annotation of the target objects that has to follow a number of specific scientific guidelines. We demonstrate that a selection of representative SAR images with subsequent feature extraction and relevance feedback classification during the generation of a classification scheme leads to a reliable definition of a new high-resolution multi-level SAR image classification scheme that can be applied globally for semantic annotation in an automated chain.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • A Modified H−α Classification Method for
           DCP Compact Polarimetric Mode by Reconstructing Quad H
           and α Parameters From Dual Ones
    • Pages: 2233 - 2241
      Abstract: Nowadays, compact polarimetry (CP), because of its great advantages, is considered as a suitable alternative to full polarimetry (FP). One of the important issues in the field of CP is directed toward extracting FP information from CP data as much as possible. In this paper, we propose a novel approach for reconstructing quad H and α parameters from dual circular polarization (DCP) compact data without reconstructing whole FP data. Consequently, it can reduce the time cost, data storage and errors introduced during the Pseudo-FP reconstruction process. We show that the proposed method performs well regarding discrimination of different physical scattering mechanisms and reconstruction of quad H and α parameters. The superiority of the new method compared with the alternatives is examined using a dataset consists of a variety of SAR sensors, frequencies and scenes with different scattering mechanisms.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir
           Channels With Time-Lapse Seismic Measurements
    • Pages: 2242 - 2254
      Abstract: Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the $ell _{2}$ -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • MTF-Based Deblurring Using a Wiener Filter for CS and MRA Pansharpening
           Methods
    • Pages: 2255 - 2269
      Abstract: Pansharpening is the fusion of low-resolution multispectral (MS) images and high-resolution panchromatic (PAN) images to yield a high-resolution MS image. The component substitution (CS) and multiresolution analysis (MRA) methods are usually computationally efficient, making them able to handle large datasets. However, these methods often produce images that suffer from spectral and spatial distortions. The CS and MRA methods can be described using general injection schemes where details extracted from the PAN image, modulated by a band-dependent gain constant, are added to the MS image, which has been interpolated to the size of the PAN image. In this paper, we propose a simple modification of these schemes where the interpolated MS image is replaced by its deblurred version, where the deblurring kernel is matched to the modulation transfer function (MTF) of the MS sensor. This can significantly enhance the quality of the fused image. Using two real datasets and one simulated dataset, our experimental results show that using the proposed preprocessing method can significantly increase both the spectral and spatial quality of the fused image according to quantitative quality metrics.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on
           Cloud Computing Architectures
    • Pages: 2270 - 2278
      Abstract: Cloud computing offers the possibility to store and process massive amounts of remotely sensed hyperspectral data in a distributed way. Dimensionality reduction is an important task in hyperspectral imaging, as hyperspectral data often contains redundancy that can be removed prior to analysis of the data in repositories. In this regard, the development of dimensionality reduction techniques in cloud computing environments can provide both efficient storage and preprocessing of the data. In this paper, we develop a parallel and distributed implementation of a widely used technique for hyperspectral dimensionality reduction: principal component analysis (PCA), based on cloud computing architectures. Our implementation utilizes Hadoop’s distributed file system (HDFS) to realize distributed storage, uses Apache Spark as the computing engine, and is developed based on the map-reduce parallel model, taking full advantage of the high throughput access and high performance distributed computing capabilities of cloud computing environments. We first optimized the traditional PCA algorithm to be well suited for parallel and distributed computing, and then we implemented it on a real cloud computing architecture. Our experimental results, conducted using several hyperspectral datasets, reveal very high performance for the proposed distributed parallel method.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Application and Evaluation of a Hierarchical Patch Clustering Method for
           Remote Sensing Images
    • Pages: 2279 - 2289
      Abstract: In this paper, we apply and evaluate a modified Gaussian-test-based hierarchical clustering method for high-resolution satellite images. The purpose is to obtain homogeneous clusters within each hierarchy level which later allow the classification and annotation of image data ranging from single scenes up to large satellite data archives. After cutting a given image into small patches and feature extraction from each patch, $k$ -means are used to split sets of extracted image feature vectors to create a hierarchical structure. As image feature vectors usually fall into a high-dimensional feature space, we test different distance metrics, to tackle the “curse of dimensionality” problem. By using three different synthetic aperture radar (SAR) and optical image datasets, Gabor texture and Bag-of-Words (BoW) features are extracted, and the clustering results are analyzed via visual and quantitative evaluations. We also compared our approach with other classic unsupervised clustering methods. The most important contributions of this paper are the discussion and evaluation of cluster homogeneity by comparing various datasets, feature descriptors, evaluation measures, and clustering methods, as well as the analysis of the clustering performances under various distance metrics. The results show that the Gaussian-test-based hierarchical patch clustering method is able to obtain homogeneous clusters, while Gabor texture features perform better than the BoW features. In addition, it turns out that a distance parameter ranging from 1.2 to 2 performs best. Also indicated by [1], our modified G-means algorithm is faster than the original algorithm.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Producing Subpixel Resolution Thematic Map From Coarse Imagery: MAP
           Algorithm-Based Super-Resolution Recovery
    • Pages: 2290 - 2304
      Abstract: Subpixel mapping (SPM) of hyperspectral remote sensing imagery is a promising technique for deriving fine mapping result by classification at fine spatial resolution. There is a type of algorithm for SPM, namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at subpixel level and then allocates classes for subpixel according to the soft attribute values. However, the fraction images derived from spectral unmixing are of less prior information of original hyperspectral remote sensing imagery and there are lots of errors in SPM result due to the limitation of spectral unmixing technology currently available. In this paper, a framework based on subpixel resolution thematic map, namely, super-resolution then classification (STC) is proposed to improve mapping result. In the proposed framework, a maximum a posteriori (MAP) model associated with the endmembers of interest (EOI), namely, T-MAP-SR is applied to the original coarse imagery to derive a high-resolution imagery with generous prior information. Then fine mapping result can be derived from the high-spatial resolution imagery by the available classification methods. Experiments show that the proposed framework can produce higher mapping accuracy result and protect the classes of interest (COI).
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Spectral-Spatial Classification Based on Affinity Scoring for
           Hyperspectral Imagery
    • Pages: 2305 - 2320
      Abstract: Recently, spectral-spatial classification for hyperspectral imagery (HSI) has become popular since it addresses the issues of limited prior knowledge and spectral internal-class variability. To provide simple and effective approaches in this area, we propose a novel supervised spectral-spatial measurement, affinity score (AS). It considers three factors: local spatial consistency, spectral similarity, and prior knowledge. It is used for classification since it can be directly designed to quantify how much a pixel belongs to a class. Furthermore, we propose two AS-based spectral-spatial classification methods such as combinational rule based on AS (CRAS) and semisupervised classifier based on AS (SCAS). CRAS creates a classification map with increased accuracy by combining spectral classification and spatial segmentation results. SCAS classifies the original HSI in a semisupervised manner. Between the two methods, SCAS is robust to the scarcity of training samples while CRAS is efficient. Experimental results show that the proposed methods can outperform several classic classifiers and state-of-the-art methods.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Supervised Hyperspectral Image Classification With Rejection
    • Authors: Condessa; F.;Bioucas-Dias, J.;Kovacevic, J.;
      Pages: 2321 - 2332
      Abstract: Hyperspectral image classification is a challenging problem as obtaining complete and representative training sets is costly, pixels can belong to unknown classes, and it is generally an ill-posed problem. The need to achieve high classification accuracy may surpass the need to classify the entire image. To account for this scenario, we use classification with rejection by providing the classifier with an option not to classify a pixel and consequently reject it. We present and analyze two approaches for supervised hyperspectral image classification that combine the use of contextual priors with classification with rejection: 1) by jointly computing context and rejection and 2) by sequentially computing context and rejection. In the joint approach, rejection is introduced as an extra class that models the probability of classifier failure. In the sequential approach, rejection results from the hidden field associated with a marginal maximum a posteriori classification of the image. We validate both approaches on real hyperspectral data.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • A Hyperheuristic Approach for Unsupervised Land-Cover Classification
    • Pages: 2333 - 2342
      Abstract: Unsupervised land-use/cover classification is of great interest, since it becomes even more difficult to obtain high-quality labeled data. Still considered one of the most used clustering techniques, the well-known $k$ -means plays an important role in the pattern recognition community. Its simple formulation and good results in a number of applications have fostered the development of new variants and methodologies to address the problem of minimizing the distance from each dataset sample to its nearest centroid (mean). In this paper, we present a genetic programming-based hyperheuristic approach to combine different metaheuristic techniques used to enhance $k$ -means effectiveness. The proposed approach is evaluated in four satellite and one radar image showing promising results, while outperforming each individual metaheuristic technique.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Semantic Classification of High-Resolution Remote-Sensing Images Based on
           Mid-level Features
    • Pages: 2343 - 2353
      Abstract: With the resolution improvement of the remote-sensing images, more details are shown clearly. The challenge that comes along is how to boost the relatively low classification accuracy caused by using pixel-based image classification approaches and low-level visual structure. The low-level features (LLF) may not well describe the image due to the semantic gap between low-level visual features and high-level semantics of images. The bag-of-visual-words (BOV) model which generates mid-level features was proposed to bridge the two levels. However, it generally neglects the context information between local patches. In this paper, an object-oriented semantic classification algorithm that combines BOV with the optimal segmentation scale is presented. In this algorithm, BOV addresses the problem of the representation of mid-level for scenes, while the optimal segmentation scale intends to overcome the defect of conventional BOV in lacking of relationship between image patches and to give more thorough description. The object-based BOV is presented to construct mid-level representations for object description instead of LLF, and histogram intersection kernel (HIK) is introduced in support vector machine (SVM) for classification. The experiments conducted on three datasets testify the superiority of the proposed algorithm.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Coalition Game Theory-Based Feature Subspace Selection for Hyperspectral
           Classification
    • Pages: 2354 - 2364
      Abstract: In this paper, an algorithm to randomly select feature subspaces for hyperspectral image classification using the principle of coalition game theory (CGT) is presented. The feature selection algorithms associated with nonlinear kernel-based support vector machines (SVM) are either NP-hard or greedy and hence, not very optimal. To deal with this problem, a metric based on the principles of CGT called Shapely value (SV) and a sampling approximation is used to determine the contributions of individual features toward the classification task. Feature subsets are randomly drawn from a probability distribution function (pdf) generated using normalized SVs of the individual features. These feature subsets are then used to build kernels corresponding to individual weak classifiers in the sparse kernel-based ensemble learning (SKEL) framework. By weighting the kernels optimally and sparsely, a small number of useful subsets of features are selected which improve the generalization performance of the ensemble classifier. The algorithm is applied on real hyperspectral datasets, and the results are presented in the paper.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Hyperspectral Airborne “Viareggio 2013 Trial” Data
           Collection for Detection Algorithm Assessment
    • Pages: 2365 - 2376
      Abstract: For many years, the entire target detection scientific community has felt the urge for fully ground-truthed hyperspectral imagery data sets expressly released for testing and comparing detection algorithms. Although a few excellent data-sharing efforts have been carried out in the last decade, the use of either restricted or not well ground-truthed imagery still remains a common practice in the target detection literature. In this paper, we provide an overview of a new hyperspectral data set that we release to the scientific community with the specific goal of fostering unbiased comparison and scientific discussions of anomaly detection (AD), object detection, and anomalous change detection (ACD) algorithms. The data set is fully ground-truthed and documented and includes scenarios and experiments specifically conceived for detection algorithm comparison and benchmarking. Insights about the various possible data exploitation tasks are provided by making reference to noise estimation and reduction, AD, spectral signature-based target detection (SSBTD), and ACD. Experimental results concerning ACD and SSBTD are presented and highlight the usefulness of this new data set from the data sharing and algorithmic comparison perspectives.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing
           and Sparse Coding
    • Pages: 2377 - 2389
      Abstract: Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyperspectral images (HSI) is limited, due to sensor limitations. In many applications, HSI with a high spectral as well as spatial resolution are required. In this paper, a new method for spatial resolution enhancement of a HSI using spectral unmixing and sparse coding (SUSC) is introduced. The proposed method fuses high spectral resolution features from the HSI with high spatial resolution features from an MSI of the same scene. Endmembers are extracted from the HSI by spectral unmixing, and the exact location of the endmembers is obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem which requires a regularization term in order to convert it into a well-posed inverse problem. As a regularizer, we employ sparse coding (SC), for which a dictionary is constructed using high spatial resolution MSI or PAN images from unrelated scenes. The proposed algorithm is applied to real Hyperion and ROSIS datasets. Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing, and SC methods, the proposed method is shown to significantly increase the spatial resolution while perserving the spectral content of the HSI.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Hyperspectral Blind Reconstruction From Random Spectral Projections
    • Pages: 2390 - 2399
      Abstract: This paper proposes a blind hyperspectral reconstruction technique termed spectral compressive acquisition (SpeCA) conceived to spaceborne sensors systems which are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. SpeCA exploits the fact that hyperspectral vectors often belong to a low-dimensional subspace and it is blind in the sense that the subspace is learned from the measured data. SpeCA encoder is computationally very light; it just computes random projections (RPs) with the acquired spectral vectors. SpeCA decoder solves a form of blind reconstruction from RPs whose complexity, although higher than that of the encoder, is very light in the sense that it requires only the modest resources to be implemented in real time. SpeCA coding/decoding scheme achieves perfect reconstruction in noise-free hyperspectral images (HSIs) and is very competitive in noisy data. The effectiveness of the proposed methodology is illustrated in both synthetic and real scenarios.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Enhancing Hyperspectral Endmember Extraction Using Clustering and
           Oversegmentation-Based Preprocessing
    • Pages: 2400 - 2413
      Abstract: Spectral mixture analysis (SMA) is an effective tool in recognition of unique spectral signatures of materials called endmembers and estimating their percentage of existence (abundance fractions). Most approaches designed in endmember extraction process are established by applying the spectral information of the dataset and, thus, tend to neglect the existing spatial correlation between adjacent pixels. Although several preprocessing modules have been developed by incorporating both spatial and spectral properties prior to spectral-based endmember extraction algorithms (EEs), they still encounter several challenges. Hence, in this paper, we propose an appropriate clustering and oversegmentation-based preprocessing (COPP) by greatly benefiting from the integration of spatial and spectral information. Moreover, a novel top-down oversegmentation (TDOS) algorithm is developed which can recognize small oversegments with high spatial correlation. Our scheme removes oversegments located at spatial border of cluster regions. Average spectral vectors of determined spatially homogenous oversegments are considered so that their spectral purity scores are calculated. COPP identifies spatially homogenous zones with the greatest spectral purity scores. Pixels of these regions are more likely to be adopted as endmembers by means of subsequent EEs. COPP can take advantage of degrading local spectral variability and noise power. The main contribution of this paper is the enhanced computational performance of EE as well as the precise reconstruction of the original hyperspectral scene besides its appropriate recognition of endmembers’ spectral signatures. The effectiveness of our design and its validation are appraised with the state-of-the-art strategies on a synthetic and AVIRIS real hyperspectral datasets.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Landfast First-Year Snow-Covered Sea Ice Reconstruction via
           Electromagnetic Inversion
    • Pages: 2414 - 2428
      Abstract: The inversion of the monostatic normalized radar cross section (NRCS) data collected by an on-site C-band scatterometer and also RADARSAT-2 satellite are investigated to reconstruct some parameters of interest associated with landfast snow-covered sea ice in Cambridge Bay, Nunavut, Canada. The parameters of interest are temperature, density, salinity, and snow grain size. To this end, this remote sensing problem is cast as an inverse scattering problem in which a data misfit cost functional is to be minimized using a differential evolution algorithm. This minimization requires repetitive calls to an appropriate electromagnetic forward solver. The utilized electromagnetic forward solver attempts to model both surface and volume scattering components associated with the irradiated rough multilayered medium under investigation. The reconstruction results demonstrate the ability of this inversion algorithm to retrieve the parameters of interest with reasonable accuracy. In particular, the best performance of the inversion algorithm occurs when both the scatterometer and satellite NRCS data are simultaneously used in the inversion process.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Firn Stratigraphic Genesis in Early Spring: Evidence From Airborne Radar
    • Pages: 2429 - 2435
      Abstract: The mapping of internal annual layers in polar firn with ultra-wideband (UWB) surface-based and airborne radars enables the assessment of past snow accumulation amounts and patterns. We used a UWB microwave radar (snow radar) to image and track internal layers in firn over hundreds of kilometers in West Antarctica (WA). We used dated ice-core records to determine that stratigraphic internal layers (SILs) are mainly formed in spring. The analysis uncertainty is generally small, with the highest values associated with regions and/or years with low accumulation. In survey regions with multiple ice-core records, the choice of ice-core record used for the radar analysis was found to have a very small contribution to the overall analysis uncertainty.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Ka-Band Mapping and Measurements of Interferometric Penetration of the
           Greenland Ice Sheets by the GLISTIN Radar
    • Pages: 2436 - 2450
      Abstract: Measuring ice surface topography over the major ice caps of Greenland and Antarctica is crucial to quantifying and understanding the effect of climate change on the Earth's environment. Multiple sensors including radars, lidars, and optical systems have been utilized in making these measurements. To integrate data from these multiple sensors into a coherent and self-consistent history of ice cap topography requires knowledge of where vertically within the snow volume the elevation measurement corresponds. This paper examines the penetration of a Ka-band cross-track interferometric radar into the dry firn at Greenland's summit using the NASA GLISTIN Ka-band interferometric radar. GLISTIN elevation measurements are compared to NASA Wallop's Airborne Terrain Mapper lidar and kinematic GPS survey measurements to assess the amount of relative penetration with GPS-surveyed corner reflectors deployed to establish the absolute vertical positioning of the radar data. We found an interferometric penetration depth estimate of 27 $pm$ 0.3 cm. Moreover, we compare these penetration measurements to model derived estimates of the amount of interferometric penetration and provide sensitivity analysis of the amount of penetration to various ice properties. Interferometric radar mapping systems also have the ability to make wide swath topographic measurements over a wide range of weather conditions either day or night making them ideal instruments for wide area mapping. We illustrate this aspect of interferometric radar mapping with a mosaic of 24 passes of the GLISTIN instrument of the Jakobshavn Glacier area.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Antarctic Sea-Ice Classification Based on Conditional Random Fields From
           RADARSAT-2 Dual-Polarization Satellite Images
    • Pages: 2451 - 2467
      Abstract: In January 2014, Chinese National Antarctic Research Expedition (CHINARE) 30th cruise raised public concern since the Xuelong, the Chinese polar research vessel, was trapped in the sea-ice zone (66°39′20.88″S, 144°25′2.28″E) in the vicinity of the Adélie Depression area on the east Antarctic continent. This event highlighted the importance of an operational sea-ice classification map for ice routing to serve ship navigation. In this paper, unprecedented Antarctic sea-ice classification algorithms from RADARSAT-2 satellite dual-polarization synthetic aperture radar (SAR) images were developed using the conditional random fields (CRF) approach by including multiple features from sea-ice concentration, gray-level cooccurrence matrix textures, polarization ratio, backscatter coefficients, and intensity data. Coincident RADARSAT-2 Satellite SAR datasets with five scenes were collected for ice classification into categories such as open water, thin ice, smooth first year ice, deformed first year ice, and old ice during the CHINARE-30th cruise. The effects of deformation, rafting, and ridging during the spring–summer transition period were overwhelmed by the spatial and contextual CRF models in combination with the rich features extracted for sea-ice classification. Four strategies including statistical distribution and region connection, multiple features and support vector machine (SVM) integrated into the CRF model are proposed to describe the sea-ice-type relationships among pixels. By conducting comparative experiments between the proposed methods and state-of-the-art sea-ice classification based on the SVM algorithm, the best was obtained from the SVM-based CRF (SVM-CRF) algorithm for sea-ice classification with respect to the three scenes from the Indian Ocean sector and two scenes of Pacific Ocean sectors including medium-resolution dual-polarization SAR imagery with a pixel spacing of 50 m and higher - esolution dual-polarization SAR imagery with a pixel spacing of 6.25 m. Results indicate that the SVM-CRF approach has the capacity for improving sea-ice classification, which can provide accurate and reliable sea-ice class information for sea-ice analysis.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Retrieving Soil Temperature at a Test Site on the Yamal Peninsula Based on
           the SMOS Brightness Temperature Observations
    • Pages: 2468 - 2477
      Abstract: In this paper, the results of radiothermal remote sensing of soil temperature at a test site on the Yamal Peninsula using full-polarimetry multiangular brightness temperature (BT) observations at the frequency of 1.4 GHz are presented. The BT data were obtained from the Soil Moisture and Ocean Salinity (SMOS) satellite with the SMOS footprint near the Polar Weather Station Marresale, the Russia Federation. The SMOS data covered the period from January 1, 2013 to December 31, 2013. The method to retrieve the soil temperature was based on solving an inverse problem by minimizing the norm of the residuals between the observed and predicted values of the BTs. The calculation of the BT was performed using a semiempirical model of radiothermal emission, which incorporated an attenuation of the microwaves in the snow pack or the canopy and a temperature-dependent multirelaxation spectral dielectric model (TD MRSDM) for an organic-rich tundra soil. The TD MRSDM was specifically designed based on laboratory measurements of the complex permittivity of the organic-rich soil samples, which were collected at the test site on the Yamal Peninsula. As a result, the values of the root-mean-square error and the determination coefficient between the retrieved and measured soil temperatures were determined to be 2.2 °C and 0.70 and 3.5 °С and 0.52, respectively, for thawed frozen soil. These results indicate the perspectives of using the full-polarimetric multiangular BT observations in the L-band for the purpose of measuring the soil temperature in the Arctic region.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Robust Assessment of an Operational Algorithm for the Retrieval of Soil
           Moisture From AMSR-E Data in Central Italy
    • Pages: 2478 - 2492
      Abstract: In this study, the soil moisture content (SMC) derived from the AMSR-E acquisitions by using the “HydroAlgo” algorithm, which is based on artificial neural networks (ANN), is compared with simulated data obtained from the application of a soil water balance model (SWBM) in central Italy. All the overpasses available for the 9-year lifetime of AMSR-E have been considered for this comparison, which was carried out point by point over a grid of 91 nodes spaced at 0.1° × 0.1°, roughly corresponding to the Umbria region. HydroAlgo includes a disaggregation technique (smoothing filter-based intensity modulation), which allowed obtaining an SMC product with enhanced spatial resolution (0.1°) that is expected to be more suitable for hydrological applications. The main purpose of this study is to exploit the potential of AMSR-E sensors for hydrological studies, and in particular for SMC monitoring on a regional scale in heterogeneous landscapes typical of Mediterranean environment. Slightly different results were obtained using ascending or descending overpasses; however, the overall correlation coefficient between the SMC retrieved by HydroAlgo and the SMC simulated by SWBM was higher than 0.8 and the corresponding root mean square error was less than 0.055 m3/m3. Based on these successful results, HydroAlgo is going to be implemented for current multifrequency microwave radiometers (AMSR2) in order to obtain a high-resolution SMC product suitable to be assimilated into flood- and landslide-related modeling in central Italy.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • SMOS Near-Surface Salinity Stratification Under Rainy Conditions
    • Pages: 2493 - 2499
      Abstract: The European Space Agency’s soil moisture ocean salinity (SMOS) satellite was launched in 2009 to measure land soil moisture and sea surface salinity (SSS). It carries an L-band microwave imaging radiometer that measures brightness temperatures that are used to produce global ocean salinity (OS) maps every three days. Similar maps are obtained with NASA’s L-band push-broom radiometer Aquarius (AQ) on board of the AQ/SAC-D satellite that was launched in 2011. In previous studies, the Central Florida Remote Sensing Laboratory (CFRSL) has analyzed AQ SSS retrievals during rain and has developed a model to predict the effect of precipitation on the SSS measurements. This rain impact model (RIM) estimates the transient near-surface salinity stratification based upon the corresponding rain accumulation over the previous 24 h to the satellite observation. In this paper, the RIM methodology has been adapted to the SMOS geometry, presenting comparisons with its SSS measurements; also, spatial correlations are performed between SMOS salinity images with those predicted by RIM for different wind speed ranges. Therefore, the main objective of this research is to better understand the processes of near-surface salinity stratification, which impact the interpretation of satellite-based SSS measurements to measure the ocean bulk salinity (5–10-m depth). The results presented in this paper show an excellent performance of RIM when applied to SMOS SSS data. Also, the SSS comparisons show that significant rain events are rapidly diluted for wind speeds of $sim 12 text{ m}/text{s}$ and above.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and
           Sentinel-1A Data
    • Pages: 2500 - 2508
      Abstract: For many applied problems in agricultural monitoring and food security, it is important to provide reliable crop classification maps. Satellite imagery is extremely valuable source of data to provide crop maps in a timely way at moderate and high spatial resolution. Information on parcel boundaries that takes into account the spatial context may improve the quality of maps compared to pixel-based classification approaches. In general, parcels may contain several plots with different crops and such situations should be taken into account when using parcel boundaries. In this paper, we aim to compare pixel-based and parcel-based approaches to crop classification from multitemporal optical (Landsat-8) and synthetic-aperture radar (SAR) Sentinel-1 imagery. For this, we propose a parcel-based approach that involves a pixel-based classification map and specifically designed rules to account for several plots within parcel. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring test site in Ukraine covering the Kyiv oblast (North of Ukraine) in 2013–2015, and the Odessa oblast (South of Ukraine) in 2014–2015. We found that pixel-based overall classification accuracy can be increased from 85.32% to 89.40% when using parcel boundaries. Among tested parcel-based approaches, the one that relied on pixel-based classification map and a procedure to select multiple plots within the parcel yielded the best performance.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Paddy-Rice Phenology Classification Based on Machine-Learning Methods
           Using Multitemporal Co-Polar X-Band SAR Images
    • Pages: 2509 - 2519
      Abstract: Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Satellites with synthetic aperture radar (SAR) sensor are highly preferred on such studies because of not only their day/night and all weather acquisition capabilities but also their ability to detect small morphological changes in monitored target, regarding the wavelength of signals. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. This paper focused on building a feasible phenology classification schema for paddy-rice using multitemporal co-polar TerraSAR-X images. Phenology classification was conducted with support vector machines (SVM) with linear and nonlinear kernel, k-nearest neighbors (kNN), and decision trees (DT). The key implementation challenges such as the number of classes, the identification of the boundaries of the classes, and the selection of textural and polarimetric features were deeply analyzed. According to all the evaluations conducted, the classification schema was finalized to be used for obtaining thematic maps for two independent rice-cultivated agricultural areas located in Spain and Turkey. The results of these experiments enable one to draw a conclusion about feasibility of machine learning (ML) algorithms in operational phenology monitoring.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • L-Band Passive and Active Signatures of Vegetated Soil: Simulations With a
           Unified Model
    • Pages: 2520 - 2531
      Abstract: The synergistic exploitation of L-band active and passive measurements has received increasing interest. In this paper, both theoretical simulations and experimental data are employed to get a deeper insight into the relations between the backscattering coefficient and the emissivity. An active/passive discrete model is used to simulate the backscattering coefficient and the emissivity of bare soil, maize crop of various heights, and deciduous forests of various biomass values. Volumetric soil moisture is varied in a 5–40% range. Simulations confirm the already known effects of soil moisture (whose increase produces a decrease in emissivity e and an increase in backscattering coefficient ${sigma ^0}$ ) and the effects of vegetation growth that yields an increase in both ${sigma ^0}$ and e. Experimental data collected by four airborne campaigns and spaceborne active/passive instruments, together with ground measurements, are also reported. Simulated data are used to investigate the sensitivity of active and passive measurements to soil moisture variations under different vegetation covers, and to estimate the coefficient of a linear relation between backscattering coefficient and emissivity.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Indirect Measurement of Forest Leaf Area Index Using Path Length
           Distribution Model and Multispectral Canopy Imager
    • Pages: 2532 - 2539
      Abstract: Spatial heterogeneity within canopies and woody components are two factors that limit the accuracy of indirect leaf area index (LAI) measurements, but they have not been fully considered because of the limitations of commercial instruments. This study combined the path length distribution model and multispectral canopy imager for the first time to improve the accuracy of indirect LAI measurements. Indirect and direct in situ measurements were conducted in broadleaf and coniferous forests. Results show that spatial heterogeneity within canopies underestimates the LAI by 16–25%, whereas woody components overestimate LAI by 14–28% in four forest sites. These two factors exhibit opposing effects, which may be misleading and may thus complicate the quantification and validation of the effect of each factor. Ignoring woody components underestimates the degree of spatial heterogeneity or clumping in forests. Considering both nonrandomness within canopies and woody components is necessary in indirect LAI measurements.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into
           an Agro-Meteorological Model to Estimate Soybean Yield
    • Pages: 2540 - 2553
      Abstract: Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient ${sigma_{{^{circ}}{textsc{vv}}}}$ $({text{LAI}}_{text{MTVI2}} $ or ( ${text{LAI}}_{sigma^{circ}{textsc{vv}}} $ ) and the dry biomass (DB) derived from the SAR Pauli matrix T33 $({text{DB}}_{{text{T}}33}) ({text{r}}^{2} > 0.83)$ , demonstrating the complementary of optical and SAR data.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Individual Tree Species Classification From Airborne Multisensor Imagery
           Using Robust PCA
    • Pages: 2554 - 2567
      Abstract: Remote sensing of individual tree species has many applications in resource management, biodiversity assessment, and conservation. Airborne remote sensing using light detection and ranging (LiDAR) and hyperspectral sensors has been used extensively to extract biophysical traits of vegetation and to detect species. However, its application for individual tree mapping remains limited due to the technical challenges of precise coalignment of images acquired from different sensors and accurately delineating individual tree crowns (ITCs). In this study, we developed a generic workflow to map tree species at ITC level from hyperspectral imagery and LiDAR data using a combination of well established and recently developed techniques. The workflow uses a nonparametric image registration approach to coalign images, a multiclass normalized graph cut method for ITC delineation, robust principal component analysis for feature extraction, and support vector machine for species classification. This workflow allows us to automatically map tree species at both pixel- and ITC-level. Experimental tests of the technique were conducted using ground data collected from a fully mapped temperate woodland in the UK. The overall accuracy of pixel-level classification was 91%, while that of ITC-level classification was 61%. The test results demonstrate the effectiveness of the approach, and in particular the use of robust principal component analysis to prune the hyperspectral dataset and reveal subtle difference among species.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Shadow Detection and Removal for Occluded Object Information Recovery in
           Urban High-Resolution Panchromatic Satellite Images
    • Pages: 2568 - 2582
      Abstract: The existence of shadows in very high-resolution panchromatic satellite images can occlude some objects to cause the reduction or loss of their information, particularly in urban scenes. To recover the occluded information of objects, shadow removal is a significant processing procedure for the image interpretation and application. In this paper, we propose a novel framework of shadow detection and removal for panchromatic satellite images to restore the obscured object information. In shadow detection, we present an automatic soft shadow detection method by the combined application of a bimodal histogram splitting method and image matting technique. Soft detection results can exhibit both umbra areas and penumbra areas to describe the shadow distribution precisely. In shadow removal, we propose a spatial adaptive nonlocal sparse shadow removal method to operate at two levels. For the initial step, we apply the line correction method to enhance shadow areas roughly in global. In the refined process, we study the characteristics of objects and shadows, and analyze spatial relationship between them. The second linear radiometric correction and nonlocal sparse model are used to simultaneously control the brightness and smoothness of the recovered shadow areas to be the same as the corresponding nonshadow areas based on group matrix with similar patches. Our method can restore the uniform objects in the shadow areas. High-resolution panchromatic shadow images of different cases and different satellites are processed by the proposed method. The experimental results verify the effectiveness and superiority of the proposed method by comparing three other methods.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Extraction of Urban Areas From Polarimetric SAR Imagery
    • Pages: 2583 - 2591
      Abstract: Polarimetric synthetic aperture radar (PolSAR) images are extensively used for land-use/land-cover (LULC) classification. One of the important issues in radar remote sensing is urban area detection, where difficulties are found because of its heterogeneity. In this paper, we are interested in urban area detection using PolSAR images which allow us detecting the scattering mechanisms by the use of polarimetric target decompositions methods. We propose in this paper two methods: in the first one, we use the powers of Yamaguchi four-component decomposition and in the second method, we use the coefficients of PolSAR covariance matrix calculated in the circular polarization basis. We added in each method the complex Wishart maximum likelihood (ML) classifier to refine the classification results. To validate both methods, we used two PolSAR images acquired in C-band by RADARSAT-2 satellite over the El Hamiz city in Algeria and San Francisco Bay. The two proposed algorithms give accurate results in both test sites, with superiority of the circular condition method.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Urban Area Extraction Using X-Band Fully Polarimetric SAR Imagery
    • Pages: 2592 - 2601
      Abstract: Here, we present a method for extracting urban areas from X-band fully polarimetric synthetic aperture radar (SAR) data by reducing the effects of polarization orientation angle (POA). The proposed classifier performs two classifications utilizing characteristics of X-band scattering in land cover. One classification uses total power of scattering and volume scattering derived by using four-component decomposition methods with correction for the POA effect. The other classification uses polarimetric coherence between $S_{ HH}$ and $S_{ VV}$ . These two results are intersected and final urban areas are extracted after postclassification processing. We applied the proposed method to airborne X-band fully polarimetric SAR data of the polarimetric and interferometric airborne synthetic aperture radar system, developed by the National Institute of Information and Communications Technology, Japan. Validation of the results for three Japanese urban areas shows that the proposed method provides an acceptable overall accuracy of approximately 80%–90% at a 100-m spatial scale. It is also shown that texture-based classifiers using single polarimetric data have accuracy limitations when applied to extracted urban areas where the POA of objects is not uniform.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Compensation for Azimuth Angle or Scale Effect on Building Extraction in
           Urban Using SAR Scales of Polarization and Coherence
    • Pages: 2602 - 2610
      Abstract: In the building extraction of urban, polarization and coherence of synthetic aperture radar (SAR) data as multiple scales were used to compensate for the azimuth angle variation that is considered as the azimuth scale effect. As scales varied from single, dual, and multiple polarizations, and to polarimetry, the delineation of buildings with azimuth angles of 90° or slightly away from 90° was satisfactory. As the complexity of building patterns increased, the use of polarized data and polarimetric data even after the deorientation procedure became ineffective. Coupled with coherence data, we introduced a model to elevate the double-bounced scattering but to suppress the volumetric scattering from buildings of variable azimuth angles. Evaluated by polarimetric data from trihedral and dihedral corner reflectors (CRs) at Goldstone, CA, USA, the model increased the double-bounced scattering but decreased the volumetric scattering only for the dihedral CRs with an azimuth angle at 45°. The model had little effect on the trihedral CRs and the dihedral CRs with an azimuth angle of 90°. Results were satisfactory in the delineation of buildings with variable azimuth angles from vegetation using L-band E-SAR data from Oberpfaffenhofen, Germany. Thus, with multiple scales expressed as the single-, dual-, and multiple-polarization data, polarimetric data, and volumetric scattering model coupled with coherence information, the azimuth scale effect was sequentially removed.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Precipitation From the Advanced Microwave Scanning Radiometer 2
    • Authors: Meyers; P.C.;Ferraro, R.R.;
      Pages: 2611 - 2618
      Abstract: The Goddard Profiling Algorithm 2010 Version 2 (GPROF2010V2) was adapted for use for the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water satellite. This study presents the validation and evaluation of the National Oceanic and Atmospheric Administration’s AMSR2 precipitation product. Rain rates calculated by GROF2010V2 meet mission accuracy requirements, with root–mean-squared error of $1.3 text{mm}{cdot} text{h}^{-1}$ and $3.6 text{mm}{cdot} text{h}^{-1}$ over ocean and land, respectively. In addition to meeting instantaneous rain rate requirements, GPROF2010V2 captures monthly and seasonal precipitation patterns relative to rain gauge analysis. Recurring problems with the algorithm over land surfaces, such as false alarms due to snow cover and surface contamination, are identified. These deficiencies can be mitigated using separate day/night screening procedures and ancillary information on daily snow cover.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Heavy Rain Forecasting by Model Initialization With LAPS: A Case Study
    • Authors: Tiesi; A.;Miglietta, M.M.;Conte, D.;Drofa, O.;Davolio, S.;Malguzzi, P.;Buzzi, A.;
      Pages: 2619 - 2627
      Abstract: Results of the assimilation of high-density data to initialize the high-resolution meteorological model MOLOCH (CNR-ISAC) are described. The local analysis and prediction system (LAPS), a mesoscale data assimilation system developed at NOAA, is applied to modeling a case study of heavy precipitation that occurred over Liguria, north-western Italy, on November 4, 2011, causing severe flood in the city of Genoa. This case is representative of some episodes that affected the region in the last few years, where the coastal orography, besides enhancing the convective uplift, contributed to the formation of convergence lines over the sea, responsible for the onset of convective cells. The present work aims at the implementation of a model-based operational short-range prediction system, with particular focus on quantitative precipitation forecasting in a time range up to 12–24 h. The use of LAPS analysis as initial condition for the MOLOCH model shows a positive impact on the intensity and distribution of the simulated precipitation with respect to the simulations where only large-scale analyses are employed as initial conditions. Effects on the models simulations are due to the assimilation of surface network data, radio-sounding profiles, radar and satellite (SEVIRI/MSG) data.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an
           Integrated Variational Data Assimilation Framework
    • Pages: 2628 - 2637
      Abstract: Variational data assimilation (VDA) is an effective technique for the estimation of land surface heat fluxes. In this method, sequences of remotely sensed land surface temperature measurements are assimilated into a dynamic surface energy balance model to estimate the key unknown parameters of the turbulent heat fluxes. Despite the advantages of the VDA technique in the retrieval of land surface heat fluxes, it suffers from a key limitation, which is its tendency to be ill posed. Moreover, unlike ensemble-based schemes, the VDA method itself does not provide estimates of the predictive uncertainty of estimated parameters and, thus, retrieved fluxes. This research addresses these shortcomings by proposing an uncertainty quantification (UQ) framework for the VDA technique. The proposed framework utilizes uncertainty analysis and analysis of error covariance approximation as a tool to quantify the uncertainty of estimated parameters and to guide the formulation of a well-posed estimation problem. It provides a calibration-free tool to assess the performance of the VDA technique in retrieving land surface heat fluxes over a range of land surfaces and climatic conditions. The UQ framework suggests that the VDA approach performs poorly over wet and highly vegetated land surface regions and when the difference between land surface and air temperature is low. Moreover, it reveals that characterizing the effect of vegetation dynamics on the bulk heat transfer coefficient reduces the correlation between unknown parameters and, hence, leads to a more robust estimation of parameters.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Preliminary Evaluation of Sentinel-1A Wind Speed Retrievals
    • Authors: Monaldo; F.;Jackson, C.;Li, X.;Pichel, W.G.;
      Pages: 2638 - 2642
      Abstract: The accuracy of wind speed retrievals from synthetic aperture radars (SARs) is strongly dependent upon the accuracy of the normalized radar cross section measurement. Here, we make a preliminary assessment of wind speed retrievals from Sentinel-1A. In particular, we use Sentinel-1A SAR imagery to compute wind speed at 500-m resolution. These measurements are averaged to 25 km and compared with advanced scatterometers (ASCAT) wind speed measurements from the METOP-A and METOP-B satellites. Spatially coincident measurements separated by less than 2 h are shown to agree to better than 2 m/s in standard deviation for wind speeds less that 20 m/s. Particularly, good agreement is found for Sentinel-1A VV-polarization measurements, though both HH- and VV-measurements exceed the 2 m/s accuracy standard.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Wind Fields From C- and X-Band SAR Images at VV Polarization in Coastal
           Area (Gulf of Oristano, Italy)
    • Pages: 2643 - 2650
      Abstract: This work deals with the spatial characteristics of the wind fields evaluated from synthetic aperture radar (SAR) images and simulated by the weather research and forecasting (WRF) atmospheric model in the Gulf of Oristano, a small coastal area about $10;text{km} times 18;text{km}$ wide in western coast of Sardinia (Western Mediterranean Sea). The SAR-derived wind fields have been obtained analyzing images of the COSMO-SkyMed, Radarsat-2, and Sentinel-1A satellites through a fully two-dimensional continuous wavelet transform (2-D-CWT) method. The analysis of the wind directions has shown that the model variability is limited if compared to that inferred by 2-D-CWT method, which mostly respects the variability evidenced by in situ data. As the use of model directions to compute the SAR wind fields is a standard in many studies, the impact on the SAR wind speed retrieval of using the model instead of the SAR-derived directions has been assessed: differences of wind speed greater than $pm$ 10% occur for about the 20% of data. The spatial variability of the SAR and model wind speed fields results quite different at both local and domain scales. The knowledge of the spatial variations of the surface wind fields can be very important for the oceanographic applications and constitutes the added value brought by SAR in the description of the coastal wind. For this reason, the SAR-derived wind fields should be taken as reference in many kind of applications.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Variability of the East Greenland Current in Fram Strait From Subdaily
           COSMO-SkyMed X-SAR Imagery
    • Pages: 2651 - 2657
      Abstract: Sea ice speed vectors were extracted from COSMO-SkyMed X-band SAR images acquired twice per day from March 2013 to December 2014 over the East Greenland current (EGC) in western Fram Strait. Only winter periods (October–May) were investigated. Maximum ice speed of 81.7 cm/s to SW was detected at 80°N 0°W between November 22 and 23, 2013. At 79°N between 7° and 5°W, sea ice speed vectors were mostly directed to S-SW but reversed to the north for short periods. Using ERA-Interim atmospheric data, the southward component of the ice speed ( ${max!.}, {+71.7}$ , ${min!.}, {-30.9};text{cm/s}$ ) was correlated ( ${rm R}=0.93$ ) to the cross-strait sea level pressure gradient. Ice speeds due to the EGC were obtained by subtracting the wind-induced ice speed component estimated by linear regression. Results for winter 2013–2014 show that the mean southward component of the EGC (9.4 cm/s, $sigma =3.0$ ) accounted more than half of the mean sea ice speed (17.7 cm/s, $sigma =7.2$ ). The EGC oscillated between 5 and 15 cm/s and sustained the southward ice export during short periods when the wind reversed to north. Satellite-based measurements of the surface EGC are the complement to hydrographic measurements in Fram Strait, but further investigation is required to understand how the apparent variability of the current is influenced by the thickness of the ice transiting in the strait or by wind induced north-to-south oscillations of the sea surface in the strait.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Statistical Models of Sea Surface Salinity in the South China Sea Based on
           SMOS Satellite Data
    • Pages: 2658 - 2664
      Abstract: Study of sea surface salinity (SSS) plays an important role in the marine ecosystem, estimation of global ocean circulation and observation of fisheries, aquaculture, coral reef, and sea grass habitats. Three statistical methods applied without considering the physical effects of the input parameters are proposed to calculate SSS from soil moisture and ocean salinity (SMOS)-measured brightness temperature (TB) values and associated auxiliary data. Using these three statistical methods, named multiple linear regression (MLR) model, principal component regression (PCR) model, and quadratic polynomial regression (QPR) model, the first predictions of daily and monthly averaged SSS are made with $1 ^circtimes1 ^circ$ spatial resolution in the South China Sea (SCS, in the study area of 4°N-25 °N, 105°E-125°E) during the period between April and June 2013. Results are compared with the corresponding SMOS SSS products and Aquarius SSS products and validated using Argo measurements. Validation results show that the root-mean-squared error (RMSE) of the QPR model is around 0.46 practical salinity units (psu) compared to 0.58 psu for Aquarius daily SSS products. World Ocean Atlas (WOA13) SSS data are also used for validation in the SCS and the QPR model gives a 0.54-psu value of RMSE, which may be compared with 0.69 psu, 0.73 psu for SMOS and Aquarius Level-3 (L3) SSS products, respectively.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Joint Interpolation of Multisensor Sea Surface Temperature Fields Using
           Nonlocal and Statistical Priors
    • Pages: 2665 - 2675
      Abstract: This paper addresses the joint analysis of multisource and multiresolution remote sensing data for the interpolation of high-resolution sea surface geophysical fields. As case-study application, we consider the interpolation of sea surface temperature (SST) fields. We propose a novel statistical model that combines two key features: an exemplar-based prior and statistical priors. The exemplar-based prior, referred to as a nonlocal prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This nonlocal prior also sets an explicit conditioning between the multisensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields that are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report the experiments on both synthetic and real SST data. These experiments demonstrate the contributions of the proposed combination of nonlocal and statistical priors to interpolate visually consistent and geophysically sound SST fields from multisource satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Height Precision of SAR Altimeter and Conventional Radar Altimeter Based
           on Flight Experimental Data
    • Pages: 2676 - 2686
      Abstract: Compared with conventional radar altimeter (CRA), synthetic aperture radar altimeter (SARAL) is a new generation radar altimeter with better height precision and along-track spatial resolution. As using synthetic aperture technique, SARAL data processing and waveform model are very different from CRA. To verify the performance and the data processing algorithm, an airborne experimental system of SARAL is developed. The experimental system was installed in Y-12 aircraft, and some valid data was obtained in October 2014. The echo data with SAR mode is obtained in the experiment and the echo data of CRA mode is extracted from SAR mode data. The two modes have the same observation targets and error sources and, then, their height measurement precisions are comparable. The results indicate that the range measurement precision of SARAL is improved about 1 times. The difference of the significant wave heights from the SAR mode data and CRA mode data is negligible.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • A Comparative Study of Operational Vessel Detectors for Maritime
           Surveillance Using Satellite-Borne Synthetic Aperture Radar
    • Pages: 2687 - 2701
      Abstract: This paper presents a comparative study among four operational detectors that work by automatically post-processing synthetic aperture radar (SAR) images acquired from the satellite platforms RADARSAT-2 and COSMO-SkyMed. Challenging maritime scenarios have been chosen to assess the detectors’ performance against features such as ambiguities, significant sea clutter, or irregular shorelines. The SAR images which form the test data are complemented with ground truth to define the reference detection configuration, which permits quantifying the probability of detection, the false alarm rate, and the accuracy of estimating ship dimensions. Although the results show that all the detectors perform well, there is no perfect detector, and a better detection system could be developed that combines the best elements from each of the single detectors. In addition to the comparison exercise, the study has facilitated the improvement of the detectors by highlighting weaknesses and providing means for fixing them.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Echo Signal Quality Analysis During HY-2A Radar Altimeter Calibration
           Campaign Using Reconstructive Transponder
    • Pages: 2702 - 2708
      Abstract: A reconstructive transponder has been utilized for the in-orbit calibration campaign of the HY-2A radar altimeter since March 2012. The precision of final calibration result is influenced by echo signal’s quality in the HY-2A altimeter’s range window. As an indicator of the signal’s quality, echo signal dwell time is analyzed considering its influence on signal quality and its uncertainty. In HY-2A altimeter calibration, the echo signal dwell time is determined by the radial orbit prediction uncertainty and the real-time signal processing mechanism of the reconstructive transponder. The real-time signal processing mechanism of the reconstructive transponder utilizes some incoming signal samples without sending echo signals before transmitting. Comparing with the length of the HY-2A altimeter’s range window, the radial orbit prediction uncertainty is large. Large radial orbit prediction uncertainty and signal processing mechanism of the reconstructive transponder are two main factors that limit the echo signal dwell time in HY-2A altimeter calibration. Finally, approaches for increasing echo signal dwell time are briefly proposed.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Development of the Reconstructive Transponder for In-Orbit Calibration of
           HY-2A Altimeter
    • Pages: 2709 - 2719
      Abstract: China’s first ocean dynamic satellite HY-2A was launched in August 2011 which carries a dual-frequency (Ku- and C-band) altimeter to measure the global mean sea level. In-orbit calibration is essential for reliably measuring sea-surface height (SSH). This paper concerns a novel approach for HY-2A altimeter in-orbit calibration using a reconstructive transponder, which is different from the existing bent-pipe transponder. The reconstructive transponder captures, tracks, and records the altimeter’s pulses, then reconstructs the pulses, and transmits them back to the altimeter. It is carried on a SUV, reduces requirement for a permanent installation site, and is much more flexible and efficient. There is an internal calibration loop to enable the stability of the transponder electronics delay and the system gain. The transponder electronics delay and system gain are obtained through an overall measurement. Another benefit of the signal reconstructive transponder is to accurately estimate the altimeter clock frequency drift. HY-2A altimeter in-orbit calibration campaign started from March 2012, until now more than 70 experiments have been carried out at several sites. On March 31, 2013, HY-2A altimeter side-A failed owing to malfunction of its ultrastable oscillator (USO). And this abnormality is monitored by the transponder. From the successful experiments, HY-2A altimeter side-A and side-B instrument bias are obtained with 1- and 4-cm precisions separately. The USO frequency drifts of the HY-2A altimeter are monitored all through the calibration campaign.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Radar Parameter Design for Geosynchronous SAR in Squint Mode and
           Elliptical Orbit
    • Pages: 2720 - 2732
      Abstract: Low-inclined elliptical orbits are recommended for geosynchronous synthetic aperture radar (GEO SAR) to map the regions of interest at middle latitudes. However, the radius variation of such orbit results in a time-variant nadir interference and slant range of illuminated regions. These factors shorten the available imaging time for a specific pulse repetition interval (PRI), which may be even shorter than the required integration time. Besides, the squint mode widely used in GEO SAR brings in the nonorthogonal and nonuniform ground resolution, which cannot be completely described by the traditional range and azimuth resolutions. To solve these problems, first, the ground resolution area is proved to be an ellipse, and the axis lengths of the resolution ellipse are used to present the worst and the best resolutions along various directions. Conditions for orthogonal and uniform ground resolutions are investigated, and analytical expressions of the resolution-related parameters are derived. Second, effects of the orbit radius variation on data acquisitions are analyzed, and a continuous PRI variation method is proposed to lengthen the available imaging time. By varying the PRI with a constant time interval, the acquisition window and transmit interference are changed with the time-variant illuminated region, which loosens the constraint of the transmit interference. Finally, the proposed ground resolution expressions and continuous PRI variation method are verified by computer simulations.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Use of the GPM Constellation for Monitoring Heavy Precipitation Events
           Over the Mediterranean Region
    • Authors: Panegrossi; G.;Casella, D.;Dietrich, S.;Marra, A.;Sano, P.;Mugnai, A.;Baldini, L.;Roberto, N.;Adirosi, E.;Cremonini, R.;Bechini, R.;Vulpiani, G.;Petracca, M.;Porcu, F.;
      Pages: 2733 - 2753
      Abstract: Precipitation retrievals exploiting the available passive microwave (PMW) observations by cross-track and conically scanning satellite-borne radiometers in the Global Precipitation Measurement (GPM) mission era are used to monitor and characterize heavy precipitation events that occurred during the Fall 2014 in Italy. Different physically based PMW precipitation retrieval algorithms are used: the Cloud Dynamics and Radiation Database (CDRD) and Passive microwave Neural network Precipitation Retrieval (PNPR), used operationally in the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF), and the National Aeronautics and Space Administration (NASA) Goddard PROFiling algorithm (GPROF). Results show that PMW precipitation retrievals from the GPM constellation of radiometers provide a reliable and quantitative description of the precipitation (instantaneous and on the daily scale) throughout the evolution of the precipitation systems in the Mediterranean region. The comparable relative errors among gauges, radar, and combination of radiometer overpasses legitimize the use of PMW estimates as a valuable and independent tool for monitoring precipitation. The pixel-based comparison with dual-polarization radars and raingauges indicates the ability of the different sensors to identify different precipitation areas and regimes ( $0.60 < ,text{POD} < 0.76; 0.28 < $ FAR $ < 0.45; 0.42 < $ ETS $ < 0.59;-1.6;text{mm}/text{h} < $ ME $ < 1.1;text{mm}/text{h}$ , with values depending on the radiometer and on the precipitation product). This is parti- ularly relevant in the presence of complex orography in proximity of coastal areas, as for the analyzed cases. The different characteristics of the radiometers (i.e., viewing geometry, spatial resolution, channel assortment) and of retrieval techniques, as well as the limitations of the ground-based reference datasets, are taken into consideration in the evaluation of the accuracy and consistency of the retrievals.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Reliable and Stable Radiometers for Jason-3
    • Pages: 2754 - 2762
      Abstract: The Jason-3 mission employs the Advanced Microwave Radiometer (AMR) to provide a tropospheric path delay measurement in support of ocean altimetry. NOAA and EUMETSAT are Jason-3 lead agencies with CNES and NASA/JPL providing implementation support. Jason-3 continues the measurements of TOPEX/Poseidon [1], [2], Jason-1, and Ocean Surface Topography Mission (OSTM)/Jason-2 supporting a multidecadal ocean topography studies, including ocean circulation, climate change, hurricane intensity forecasts, and sea level change. The objective of the Jason-3 AMR is to measure the columnar water vapor in the path of the Poseidon radar altimeter (CNES instrument) to correct for water in the atmospheric path delay in the altimeter range measurement. In this paper, the design and performance of AMR are described along with the changes made compared to the predecessor Jason-2 instrument to reduce development risks and improve the stability of the AMR instrument.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Validation of Aquarius Soil Moisture Products Over the Northwest of Spain:
           A Comparison With SMOS
    • Authors: Gonzalez-Zamora; A.;Sanchez, N.;Martinez-Fernandez, J.;
      Pages: 2763 - 2769
      Abstract: A validation of the new L2 and L3 soil moisture products from the Aquarius/SAC-D mission from August 2011 to June 2014 using two in situ networks in Spain was conducted. The first network, the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), is considered to be a dense network. The second network (Inforiego) could be considered a sparse or large-scale network. Comparisons of temporal series using different strategies were made. Similar analysis was performed for the same area and period with two soil moisture ocean salinity (SMOS) soil moisture products: SMOS L2 and SMOS Barcelona Expert Center (BEC) L3. The aim of the study was to analyze the performance of the Aquarius soil moisture products and to compare with that of SMOS soil moisture. Results from the area-averaged comparison show that Aquarius products have correlation coefficients (R) between 0.33 and 0.65, and root-mean-square difference (RMSD) and centered RMSD (cRMSD) between 0.046 and $0.111,text{m}^3text{m}^{-3}$ . A better match was found for the L2 ascending series than for the L2 descending and L3 series. A dry bias was found. SMOS products showed better accuracy $(text{R} > 0.8$ , RMSD and $text{cRMSD}sim 0.06 text{m}^{3}text{m}^{-3}$ ) than those of Aquarius. The comparison made at point-scale reflected that the size and density of the networks do not influence the validation results at the Aquarius resolution, but it is remarkable at the SMOS resolution. Despite the scale restrictions, the results of this study showed that Aquarius soil moisture products have reasonably good performance.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Onboard Radar Processor Development for Rapid Response to Natural Hazards
    • Pages: 2770 - 2776
      Abstract: The unique capabilities of imaging radar to penetrate cloud cover and collect data in darkness over large areas at high resolution makes it a key information provider for the management and mitigation of natural and human-induced hazards. Researchers have demonstrated the use of UAVSAR data to determine flood extent, forest fire extent, lava flow, and landslide. Data latency of at most 2–3 h is required for the radar data to be of use to the disaster responders. We have developed a UAVSAR on-board processor for real time and autonomous operations that has high fidelity and accuracy to enable timely generation of polarimetric and interferometric data products for rapid response applications. This on-board processor design provides a space-qualification path for technology infusion into future space missions in a high-radiation environment with modest power and weight allocations. The processor employs a hybrid architecture where computations are divided between field-programmable gate arrays, which are better suited to rapid, repetitious computations, and a microprocessor with a floating-point coprocessor that is better suited to the less frequent and irregular computations. Prior to implementing phase preserving processor algorithms in FPGA code, we developed a bit-true processor model in MATLAB that is modularized and parameterized for ease of testing and the ability to tradeoff processor design with performance. The on-board processor has been demonstrated on UAVSAR flights.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • On the Use of Gaussian Random Processes for Probabilistic Interpolation of
           CubeSat Data in the Presence of Geolocation Error
    • Pages: 2777 - 2793
      Abstract: With their greatly reduced sizes, low development cost, and rapid construction time, CubeSats have merged as a platform of considerable interest for a wide range of applications, including remote sensing. Many applications require the interpolation of sensor data into a regularly spaced grid for the development of downstream scientific products. This problem is complicated for CubeSat platforms due to potentially significant uncertainties associated with the spatial position of the satellite. In this paper, we present a probabilistic approach to the data interpolation problem in which we estimate both the platform location and data samples on a regular grid given observations corrupted by noise and location error. Our approach is based on a Gaussian process model to connect the measured data to the values on the grid. Two statistical models for positional uncertainties are considered, one based on an assumption of independent errors and another motivated by positional errors associated with a specific platform of interest, the MicroMAS radiometer. In each case, the maximum a posteriori estimate of the positions and the data is generated using an optimized Gaussian process regression (OGPR) method resulting in two algorithms: OGPR-IID and OGPR-PCA. The performance of this approach is tested on both simulated data and advanced technology microwave sounder data where significant improvements both qualitatively and quantitatively relative to traditional interpolation methods are observed.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • A Prototype System for Flood Monitoring Based on Flood Forecast Combined
           With COSMO-SkyMed and Sentinel-1 Data
    • Authors: Boni; G.;Ferraris, L.;Pulvirenti, L.;Squicciarino, G.;Pierdicca, N.;Candela, L.;Pisani, A.R.;Zoffoli, S.;Onori, R.;Proietti, C.;Pagliara, P.;
      Pages: 2794 - 2805
      Abstract: The use of synthetic aperture radar (SAR) data is presently well established in operational services for flood management. However, some events might be missed because of the limited area that can be observed through a SAR image and the need of programming SAR acquisitions in advance. To tackle these problems, it is possible to setup a system that is able to trigger the SAR acquisitions based on flood forecasts and to take advantage of the various satellite SAR sensors that are presently operating. On behalf of the Italian Civil Protection Department (DPC), a prototype of this kind of system has been setup and preliminary tested, using COSMO-SkyMed (CSK) and Sentinel-1 (S-1) data, to monitor the Po River (Northern Italy) flood occurred in November 2014. This paper presents the prototype system and describes in detail the near real-time flood mapping algorithm implemented in the system. The algorithm was previously developed to classify CSK images, and is modified here in order to be applied to S-1 data too. The major outcomes of the monitoring of the Po River flood are also analyzed in this paper, highlighting the importance of the in advance programming of the radar acquisitions. Results demonstrate the reliability of the flood predictions provided by the model and the accuracy of the flood mapping algorithm. It is also shown that, when CSK and S-1 data are simultaneously acquired, their joint use allows for an interpretation of some ambiguous radar signatures in agricultural areas.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Development and Validation of Fire Damage-Severity Indices in the
           Framework of the PREFER Project
    • Pages: 2806 - 2817
      Abstract: PREFER (space-based information support for prevention and recovery of forest fires emergency in the Mediterranean area) is one of the Copernicus FP7 Emergency projects funded in 2012. It is uniquely devoted to forest pre- and post-fire management. The overall goal of the project is to develop and demonstrate a preoperational portfolio of products, based on Earth observation data for helping fire management on a Mediterranean scale. Samples of the PREFER information products are available to stakeholders through the project Geoserver (prefer.cgspace.it). The project foresees the utilization of satellite images’ optical and SAR at medium (MODIS—moderate resolution imaging spectroradiometer), high (Landsat, Spot-Satellite Pour l’Observation de la Terre), and very high (KOMPSAT—Korea Multi-Purpose Satellite, RapidEye, Pleiades, COSMO-SkyMed—constellation of small satellites for Mediterranean basin observation, and TanDEM-X—TerraSAR-X add on for digital elevation measurement) spatial resolution, and a refresh rate of the products varying from high (days) to low (twice a month) to very low (once a year). The present paper is devoted to introducing the methodology developed for computing one of the project product, i.e., the damage-severity map. These maps provide the level of damage caused in vegetated areas by fires. Further, the paper aims at presenting the results of the validation of such product carried out during the first semester of 2015. The methodology is based on the utilization of Landsat8/OLI images.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and
           Temperature Trends
    • Pages: 2818 - 2829
      Abstract: Recent climate trends evidence a rise of temperatures and an increase in the duration and intensity of droughts which is in turn leading to the occurrence of larger wildfires, which threaten the environment as well as human lives and beings. In this context, improved wildfires prediction tools are urgently needed. In this paper, the use of remotely sensed soil moisture data as a key variable in the climate-wildfires relationship is explored. The study is centered in the fires registered in the Iberian Peninsula during the period 2010–2014. Their prior-to-occurrence surface moisture-temperature conditions were analyzed using SMOS-derived soil moisture data and ERA-Interim land surface temperature reanalysis. Results showed that moisture and temperature conditions limited the extent of wildfires, and a potential maximum burned area per moisture-temperature paired values was obtained (R 2 = 0.43). The model relating fire extent with moisture-temperature preconditions was improved by including information on land cover, regions, and the month of the fire outbreak (R 2 = 0.68). Model predictions had an accuracy of 83.3% with a maximum error of 40.5 ha. Results were majorly coherent with wildfires behavior in the Iberian Peninsula and reflected the duality between Euro-Siberian and Mediterranean regions in terms of expected burned area. The proposed model has a promising potential for the enhancement of fire prevention services.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • Climate-Related Child Undernutrition in the Lake Victoria Basin: An
           Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation
           Data
    • Pages: 2830 - 2835
      Abstract: Despite growing research into the socio-economic aspects of vulnerability [1]–[4], relatively little work has linked population dynamics with climate change beyond the complex relationship between migration and climate change [5]. It is likely, however, that most people experience climate change in situ, so understanding the role of population dynamics remains critical. How a given number of people, in a given location and with varying population characteristics may exacerbate or mitigate the impacts of climate change or how, conversely, they may be vulnerable to climate change impacts are basic questions that remain largely unresolved [6]. This paper explores where and to what extent population dynamics intersect with high exposure to climate change. Specifically, in Eastern Africa's Lake Victoria Basin (LVB), a climate change/health vulnerability hotspot we have identified in prior research [7], we model child undernutrition vulnerability indices based on climate variables, including proxy measures (NDVI) derived from satellite imagery, at a 5-km spatial resolution. Results suggest that vegetation changes associated with precipitation decline in rural areas of sub-Saharan Africa can help predict deteriorating child health.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
  • CALL FOR PAPERS
    • Pages: 2836 - 2836
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: June 2016
      Issue No: Vol. 9, No. 6 (2016)
       
 
 
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