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 Subjects -> ELECTRONICS (Total: 156 journals)
 Showing 1 - 200 of 277 Journals sorted alphabetically Advances in Biosensors and Bioelectronics       (Followers: 6) Advances in Electrical and Electronic Engineering       (Followers: 2) Advances in Magnetic and Optical Resonance       (Followers: 9) Advances in Microelectronic Engineering       (Followers: 13) Advances in Power Electronics       (Followers: 26) Aerospace and Electronic Systems, IEEE Transactions on       (Followers: 219) American Journal of Electrical and Electronic Engineering       (Followers: 24) Annals of Telecommunications       (Followers: 7) Archives of Electrical Engineering       (Followers: 12) Autonomous Mental Development, IEEE Transactions on       (Followers: 8) Bell Labs Technical Journal       (Followers: 23) Biomedical Engineering, IEEE Reviews in       (Followers: 17) Biomedical Engineering, IEEE Transactions on       (Followers: 31) Biomedical Instrumentation & Technology       (Followers: 6) Broadcasting, IEEE Transactions on       (Followers: 10) BULLETIN of National Technical University of Ukraine. 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RADIOAPPARATUS BUILDING       (Followers: 1) Bulletin of the Polish Academy of Sciences : Technical Sciences Canadian Journal of Remote Sensing       (Followers: 41) China Communications       (Followers: 7) Circuits and Systems       (Followers: 16) Consumer Electronics Times       (Followers: 6) Control Systems       (Followers: 182) Edu Elektrika Journal Electronic Design       (Followers: 79) Electronic Markets       (Followers: 8) Electronic Materials Letters       (Followers: 2) Electronics       (Followers: 61) Electronics and Communications in Japan       (Followers: 8) Electronics For You       (Followers: 64) Electronics Letters       (Followers: 25) Embedded Systems Letters, IEEE       (Followers: 40) Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage       (Followers: 3) Energy Storage Materials       (Followers: 1) EPJ Quantum Technology EURASIP Journal on Embedded Systems       (Followers: 12) Facta Universitatis, Series : Electronics and Energetics Foundations and Trends® in Communications and Information Theory       (Followers: 7) Foundations and Trends® in Signal Processing       (Followers: 10) Frequenz       (Followers: 1) Frontiers of Optoelectronics       (Followers: 1) Geoscience and Remote Sensing, IEEE Transactions on       (Followers: 154) Haptics, IEEE Transactions on       (Followers: 3) IEEE Antennas and Propagation Magazine       (Followers: 76) IEEE Antennas and Wireless Propagation Letters       (Followers: 64) IEEE Journal of Emerging and Selected Topics in Power Electronics       (Followers: 35) IEEE Journal of the Electron Devices Society       (Followers: 9) IEEE Journal on Exploratory Solid-State Computational Devices and Circuits       (Followers: 1) IEEE Power Electronics Magazine       (Followers: 50) IEEE Transactions on Antennas and Propagation       (Followers: 55) IEEE Transactions on Automatic Control       (Followers: 51) IEEE Transactions on Circuits and Systems for Video Technology     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and Intelligent Systems       (Followers: 2) International Journal of High Speed Electronics and Systems International Journal of Image, Graphics and Signal Processing       (Followers: 10) International Journal of Nano Devices, Sensors and Systems       (Followers: 6) International Journal of Nanoscience       (Followers: 1) International Journal of Numerical Modelling: Electronic Networks, Devices and Fields       (Followers: 3) International Journal of Power Electronics       (Followers: 16) International Journal of Review in Electronics & Communication Engineering       (Followers: 4) International Journal of Sensors, Wireless Communications and Control       (Followers: 7) International Journal of Systems, Control and Communications       (Followers: 4) International Journal of Wireless and Microwave Technologies       (Followers: 6) International Journal on Communication       (Followers: 13) International Journal on Electrical and Power Engineering       (Followers: 8) International Transaction of Electrical and Computer Engineers System       (Followers: 2) Journal of Biosensors & Bioelectronics       (Followers: 3) Journal of Advanced Dielectrics       (Followers: 1) Journal of Artificial Intelligence       (Followers: 9) Journal of Circuits, Systems, and Computers       (Followers: 4) Journal of Computational Intelligence and Electronic Systems       (Followers: 1) Journal of Electrical and Electronics Engineering Research       (Followers: 19) Journal of Electrical Bioimpedance       (Followers: 2) Journal of Electrical Engineering & Electronic Technology       (Followers: 7) Journal of Electromagnetic Analysis and Applications       (Followers: 6) Journal of Electromagnetic Waves and Applications       (Followers: 5) Journal of Electronic Design Technology       (Followers: 6) Journal of Electronics (China)       (Followers: 4) Journal of Energy Storage       (Followers: 2) Journal of Field Robotics       (Followers: 2) Journal of Guidance, Control, and Dynamics       (Followers: 140) Journal of Intelligent Procedures in Electrical Technology       (Followers: 3) Journal of Low Power Electronics       (Followers: 7) Journal of Low Power Electronics and Applications       (Followers: 7) Journal of Microwaves, Optoelectronics and Electromagnetic Applications       (Followers: 9) Journal of Nuclear Cardiology Journal of Optoelectronics Engineering       (Followers: 2) Journal of Physics B: Atomic, Molecular and Optical Physics       (Followers: 31) Journal of Power Electronics & Power Systems       (Followers: 9) Journal of Semiconductors       (Followers: 4) Journal of Sensors       (Followers: 22) Journal of Signal and Information Processing       (Followers: 9) Jurnal Rekayasa Elektrika Learning Technologies, IEEE Transactions on       (Followers: 14) Magnetics Letters, IEEE       (Followers: 7) Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology       (Followers: 2) Metrology and Measurement Systems       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 Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of   [SJR: 1.196]   [H-I: 37]   [50 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1939-1404    Published by IEEE  [191 journals]
• Frontcover
• Abstract: Presents the front cover for this issue of the publication.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• IEEE Geoscience and Remote Sensing Society
• Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Information for Authors
• Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Institutional Listings
• Abstract: Presents a listing of institutional institutions relevant for this issue of the publication.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE
• Authors: L. Mou;X. Zhu;M. Vakalopoulou;K. Karantzalos;N. Paragios;B. Le Saux;G. Moser;D. Tuia;
Pages: 3435 - 3447
Abstract: In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Evaluation of the NDVI-Based Pixel Selection Criteria of the MODIS C6 Dark
Target and Deep Blue Combined Aerosol Product
• Authors: Muhammad Bilal;Janet E. Nichol;
Pages: 3448 - 3453
Abstract: The moderate resolution and imaging spectroradiometer (MODIS) Collection 6 (C6) level 2 operational aerosol product (MOD04) contains the Dark Target (DT) and Deep Blue (DB) combined aerosol optical depth (AOD) observations (DTB) at 10 km resolution, which is generated using the selection criteria based on the static normalized difference vegetation index (NDVI) as follows: 1) the DT AOD data are used for NDVI > 0.3; 2) the DB AOD data are used for NDVI < 0.2; and 3) the average of both algorithms or AOD data with highest quality flag are used for ≤ 0.2 NDVI ≤ 0.3. The objective of this study is to evaluate the NDVI pixel selection criteria used in the DTB AOD product. For this, the DT, the DB, and the DTB AOD retrievals are evaluated using the Aerosol Robotic Network (AERONET) level 2.0 cloud-screened and quality-controlled AOD data over Beijing from 2002 to 2014, Lahore from 2007 to 2013, and Paris from 2005 to 2014. The DT and DB AOD retrievals considered by the DTB product are tabulated. For comparison purposes, the MODIS level 3 monthly NDVI product (MOD13A3) at 1 km resolution is also tabulated indicating how the NDVI-based pixel selection criteria operate for the DT and DB AOD retrievals used in the DTB product. Results show that the DT AOD retrievals for NDVI ≤ 0.3 are used in the DTB product, and this increases the mean bias and percentage of retrievals above the expected error. These results conclude that the DTB AOD product must follow the dynamic NDVI values for pixel selection criteria.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• A New Air Pollution Source Identification Method Based on Remotely Sensed
Aerosol and Improved Glowworm Swarm Optimization
• Authors: Yunping Chen;Shudong Wang;Weihong Han;Yajv Xiong;Wenhuan Wang;Ling Tong;
Pages: 3454 - 3464
Abstract: Air pollution sources generally cannot be identified as the specific factories but certain industries. Focusing on this issue, a new method, based on an improved glowworm swarm optimization and remotely sensed imagery, was proposed to precisely orientate and quantify air pollution sources in this study. In addition, meteorological data and GIS information were also used to backtrack the pollution source. After that, in order to quantify the pollution of each factory in the study areas, three pollution indices, pollution gross (PG), pollution intensity, and area-normalized pollution (ANP), were proposed. As a result, the polluting contribution of each factory was listed, and the most polluting factories, which were bulletined as the key monitoring factories by the local authority, were accurately extracted. Among the pollution indices, ANP is the most robust, reliable, and recommended. Furthermore, the result also shows factory pollution background information achieved from the historical remote sensing data which can be used to improve the precision of identification. To our knowledge, this study provides the first attempt to address the problem of identifying a pollution source as originating from an individual factory based on remote sensing data. The proposed method provides a useful tool for air quality management, and the result would be meaningful to environmental and economic issue.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Automated Generation of Lakes and Reservoirs Water Elevation Changes From
• Authors: Modurodoluwa Adeyinka Okeowo;Hyongki Lee;Faisal Hossain;Augusto Getirana;
Pages: 3465 - 3481
Abstract: Limited access to in-situ water level data for lakes and reservoirs have been a major setback for regional and global studies of reservoirs, surface water storage changes, and monitoring the hydrologic cycle. Processing satellite radar altimetry data over inland water bodies on a large scale has been a cumbersome task primarily due to the removal of contaminated measurements as a result of surrounding land. In this study, we proposed a new algorithm to automatically generate time series from raw satellite radar altimetry data without user intervention. With this method, users with a little knowledge on the field can now independently process radar altimetry for diverse applications. The method is based on K-means clustering, interquartile range, and statistical analysis of the dataset for outlier detection. Jason-2 and Envisat radar altimetry data were used to demonstrate the capability of this algorithm. A total of 37 satellite crossings over 30 lakes and reservoirs located in the U.S., Brazil, and Nigeria were used based on the availability of in-situ data. We compared the results against in-situ data and root-mean-square error values ranged from 0.09 to 1.20 m. We also confirmed the potential of this algorithm over rivers and wetlands using the southern Congo River and Everglades wetlands in Florida, respectively. Finally, the different retracking algorithms in Envisat; Ice-1, Ice-2, Ocean, and Sea-Ice were compared using the proposed algorithm. Ice-1 performed best for generating water level time series for in-land water bodies and the result is consistent with previous studies.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Small Reservoirs Extraction in Semiarid Regions Using Multitemporal
• Authors: Donato Amitrano;Gerardo Di Martino;Antonio Iodice;Daniele Riccio;Giuseppe Ruello;
Pages: 3482 - 3492
Abstract: In this paper, we introduce a novel framework for small reservoirs extraction in semiarid environment. The task is accomplished through the introduction of a pseudoprobability index derived from multitemporal synthetic aperture radar RGB images. These products are characterized by the ease of interpretation for nonexpert users, and the possibility to be processed using simple algorithms, allowing, in this case, for the definition of an ad hoc band ratio for feature extraction. The reliability of the proposed approach is demonstrated through a case study in Burkina Faso in which 19 reservoirs up to about 6000 m$^2$ extent were tested. The obtained accuracy with respect to the available ground truth is higher than 88%.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Improved Sea Surface Height From Satellite Altimetry in Coastal Zones: A
Case Study in Southern Patagonia
• Authors: Loreley Selene Lago;Martin Saraceno;Laura A. Ruiz-Etcheverry;Marcello Passaro;Fernando Ariel Oreiro;Enrique Eduardo D'Onofrio;Raúl A. González;
Pages: 3493 - 3503
Abstract: High-resolution 20-Hz Jason-2 satellite altimetry data obtained from crossing tracks numbered 52 and 189 in San Matias Gulf, Argentina, are compared with a 22-month-long time series of sea level measured by a bottom pressure recorder. It was deployed 1.3 km from the nominal intersection of the two tracks and 0.9 km from the coast. Results show that by improving retracking and tidal modeling, satellite altimetry data become more accurate close to the coast. Indeed, a larger number of reliable data are obtained up to 1.6 km from the coast when satellite data are retracked using adaptive leading edge subwaveform retracker (ALES) rather than using the classic Brown model. The tidal model that showed the lowest root sum square (RSS) of the difference between the in situ and the modeled tidal amplitude and phase is TPXO8 (RSS 4.8 cm). Yet, the lowest difference from in situ tidal constituents is obtained by harmonic analysis of the available 23-year-long 1-Hz altimetry dataset (RSS 4.1 cm), highlighting the potential of altimetry data to compute tides. Considering ALES retracking and TPXO8 tidal correction for the 20-Hz Jason-2 data, we finally show that it is possible to retrieve 70% more data and to improve correlation with in situ measurements from 0.79 to 0.95. The sea level anomaly obtained this way has a root mean square difference from in situ data of only 13 cm as close as 4 km from the coast. Overall, the analysis performed indicates that satellite altimetry data can be greatly improved, even in complex macrotidal coastal regions.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Arctic Sea Ice Characterization Using RISAT-1 Compact-Pol SAR Imagery and
Feature Evaluation: A Case Study Over Northeast Greenland
• Authors: Suman Singha;Rudolf Ressel;
Pages: 3504 - 3514
Abstract: Synthetic Aperture Radar (SAR) polarimetry has become a valuable tool in space-borne SAR-based sea ice analysis. The two major objectives in SAR-based remote sensing of sea ice are, on the one hand, to have a large coverage and, on the other hand, to obtain a radar response that carries as much information as possible in order to characterize sea ice. Single-polarimetric acquisitions of existing sensors offer a wide coverage on the ground, whereas dual polarimetric or even better fully polarimetric data offer a higher information content, which allows for a more reliable automated sea ice analysis at a cost of smaller swath. In order to reconcile the advantages of fully polarimetric acquisitions with the higher ground coverage of acquisitions with fewer polarimetric channels, hybrid/compact polarimetric acquisitions offer an excellent tradeoff between the mentioned objectives. With the advent of the RISAT-1 satellite platform, we are able to explore the potential of compact dual pol acquisitions for sea ice analysis and classification. Our algorithmic approach for an automated sea ice classification consist of two steps. In the first step, we perform a feature extraction followed by a feature evaluation procedure. The resulting feature vectors are then ingested into a trained artificial neural network classifier to arrive at a pixel-wise supervised classification. We present a comprehensive polarimetric feature analysis and classification results on a dataset acquired off the eastern Greenland coast, along with comparisons of results obtained from near-coincident (spatially and temporally) C -band fully polarimetric imagery acquired by RADARSAT-2.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Classification of Urban Building Type from High Spatial Resolution Remote
Sensing Imagery Using Extended MRS and Soft BP Network
• Authors: Junfei Xie;Jianhua Zhou;
Pages: 3515 - 3528
Abstract: This study presents a new approach for classification of building type in complex urban scene. The approach consists of two parts: extended multiresolution segmentation (EMRS) and soft classification using BP network (SBP). The technology scheme is referred to here as EMRS-SBP. EMRS is used to guide the design of descriptor. A descriptor is a feature expression or a symbolized algorithm to systematically promote the expressing capability of image features. A classifier can perform far better to discern complex pattern of combining pixels working in an EMRS-based feature space constructed by a number of such descriptors. SBP serves as a classifier model to generate natural clusters of member which refers to here as both pixels and image patches. Class-mark ensured member is denoted as sure member and the rest as unsure (fuzzy) members. The latter can be relabeled through recursive defuzzifying according to the information carried by the gradually increased sure members. By using EMRS-SBP, three building types, i.e., old-fashioned courtyard dwellings, multistorey residential buildings, and high-rise buildings, can be accurately classified from high spatial resolution imagery in a feature space constructed with fifteen descriptors including nine EMRS-based ones. There is evidence that the mean overall accuracy using SBP in the EMRS-based feature space is 19.8% higher than that using the hard classification with BP network in a single resolution segmentation space and meanwhile, the mean kappa statistic value (κ) is 25.1% higher.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Retrieval of Specific Leaf Area From Landsat-8 Surface Reflectance Data
Using Statistical and Physical Models
• Authors: Abebe Mohammed Ali;Roshanak Darvishzadeh;Andrew K. Skidmore;
Pages: 3529 - 3536
Abstract: One of the key traits in the assessment of ecosystem functions is a specific leaf area (SLA). The main aim of this study was to examine the potential of new generation satellite images, such as Landsat-8 imagery, for the retrieval of SLA at regional and global scales. Therefore, both statistical and radiative transfer model (RTM) inversion approaches for estimating SLA from the new Landsat-8 product were evaluated. Field data were collected for 33 sample plots during a field campaign in summer 2013 in the Bavarian Forest National Park, Germany, while Landsat-8 image data concurrent with the time of field campaign were acquired. Estimates of SLA were examined using different Landsat-8 spectral bands, vegetation indices calculated from these bands, and the inversion of a canopy RTM. The RTM inversion was performed utilizing continuous wavelet analysis and a look-up table (LUT) approach. The results were validated using R2 and the root-mean-square error (RMSE) between the estimated and measured SLA. In general, SLA was estimated accurately by both statistical and RTM inversion approaches. The relationships between measured and estimated SLA using the enhanced vegetation index were strong (R2 = 0.77 and RMSE = 4.44%). Furthermore, the predictive model developed from combination of the wavelet features at 654.5 nm (scale 9) and 2200.5 nm (scale 2) correlated strongly with SLA (R2 = 0.79 and RMSE = 7.52%). The inversion of LUT using a spectral subset consisting of bands 5, 6, and 7 of Landsat-8 (R2 = 0.73 and RMSE = 5.33%) yielded a higher accuracy and precision than any other spectral subset. The findings of this study provide insights into the potential of the new generation of multispectral-medium-resolution satellite imagery, such as Landsat-8 and Sentinel-2, for accurate retrieval and mapping of SLA using either statistical or RTM inversion methods.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

Data with Trend Surface Analysis
• Authors: Xuejiao Bai;Pengxin Wang;Hongshuo Wang;Yi Xie;
Pages: 3537 - 3546
Abstract: Drought causes great losses in regional agricultural production and decreases socioeconomic growth. The vegetation temperature condition index (VTCI) has a distinct advantage in monitoring the onset, duration, and intensity of droughts. With the development of modern remote sensing technologies, remotely sensed data with variable spatial and temporal resolution are used to generate multiscale maps of droughts. Therefore, understanding the scale effect and developing appropriate up-scaling methods to retrieve spatiotemporal drought variables across different scales is valuable. As an alternative to the commonly used window averaging (WA) method, we develop the trend surface analysis (TSA) method based on multiple regression analysis to up-scale Landsat-derived VTCI (Landsat-VTCI) images from a finer to a coarser resolution. The two methods are systematically evaluated in a case study according to various statistical indicators, including the spatial and frequency distributions of features, and the correlation coefficients and root mean square errors between up-scaled Landsat-VTCI images and moderate-resolution Imaging Spectroradiometer (MODIS)-derived VTCI (MODIS-VTCI) images. The results show that TSA is reliable and more suitable than WA for non-normally distributed Landsat-derived VTCIs, whereas the WA results are similar to the TSA results for normal distributions. The TSA method is flexible for any type of distribution of Landsat-VTCIs within a study area and can be programmed to up-scale spatial drought variables from a finer to a coarser spatial resolution because of its efficiency and flexibility compared to the WA method.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• A Backprojection-Based Imaging for Circular Synthetic Aperture Radar
• Authors: Leping Chen;Daoxiang An;Xiaotao Huang;
Pages: 3547 - 3555
Abstract: Circular synthetic aperture radar (CSAR) has attracted much attention in the field of high-resolution SAR imaging. However, the CSAR image focusing is affected by the motion deviations of platform. In the processing of experimental CSAR data to deal with motion errors, the main way is using setup calibrators, which restricts its widespread applications. In this paper, based on the estimation of motion errors, an autofocus CSAR imaging strategy is proposed without using any setup calibrator. The first step is to split the entire aperture into several subapertures, the second step is to process the data in subapertures with an autofocus backprojection method, and the last step is to obtain the final CSAR image by merging the subimages obtained from the subaperture processing. The CSAR data processing results prove that the proposed strategy can remove the motion errors accurately and acquire well-focused CSAR images.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Unsupervised PolSAR Image Classification and Segmentation Using Dirichlet
Process Mixture Model and Markov Random Fields With Similarity Measure
• Authors: Wanying Song;Ming Li;Peng Zhang;Yan Wu;Lu Jia;Lin An;
Pages: 3556 - 3568
Abstract: The Markov random fields (MRF) is skillful in incorporating the spatial-contextual information of images and has been widely applied to remote-sensing image classification and segmentation. However, the traditional MRF-based method is unable to determine the precise number of clusters automatically. It is known that the Dirichlet process mixture model (DPMM) takes the number of clusters as a model parameter and estimates it in image classification. Therefore, the DPMM is a powerful and potential method for classification tasks. Then, in this paper, by fusing the DPMM model and a similarity measure scheme into the MRF framework, we propose a novel unsupervised classification and segmentation method for polarimetric synthetic aperture radar (PolSAR) images, abbreviated as DPMM-SMMRF. First, the DPMM built by the multidimensional Gaussian distribution is introduced into the MRF framework, which enables the proposed DPMM-SMMRF model to identify the underlying number of clusters automatically. Second, to utilize the polarization information adequately and modulate the spatial correlation, the similarity measure between the neighboring polarimetric covariance matrices is utilized to construct the interaction term; thus, providing strong noise immunity and enhancing the ability of the classification of the sample pixels. Then, for updating the class labels and the parameters in the proposed DPMM-SMMRF model, we propose a detailed sampling procedure based on the Gibbs sampling. Experiments on real PolSAR images demonstrate that the proposed DPMM-SMMRF model can automatically recognize the number of clusters and simultaneously obtain higher classification accuracy, more accurate edge location, and smoother homogeneous areas compared to several recent MRF models.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Demonstration of Dual-Channel TOPS SAR Imaging With Airborne C-Band Data
• Authors: Huaitao Fan;Zhimin Zhang;Robert Wang;Ning Li;Wei Xu;Zhen Xu;
Pages: 3569 - 3581
Abstract: Multichannel in azimuth synthetic aperture radar (SAR) operating in the terrain observation by progressive scans (TOPS) acquisition mode has attracted much attention recently for its capability to achieve ultrawide-swath imaging with a high spatial resolution. In order to verify the feasibility and operability of this newly developed remote sensing concept, a C-band airborne azimuth dual-channel TOPS SAR has been designed by the Institute of Electronics, Chinese Academy of Sciences, as a test bed for future spaceborne realizations. This paper introduces the experimental SAR system and reports the data processing results of an outfield experiment conducted in late September 2014. The importance of the experiment resides in its potential to validate several important technical aspects of this novel SAR operation with real experimental data, including channel mismatch cancellation and unambiguous signal reconstruction. Besides, two kinds of processing methods are proposed to calibrate the influence of antenna phase center fluctuation occurred in the dual-channel TOPS SAR. Finally, the experimental results obtained, including the phase mismatch cancellation and the focused imageries, are presented and analyzed.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Monitoring Land Subsidence in a Rural Area Using a Combination of ADInSAR
and Polarimetric Coherence Optimization
Pages: 3582 - 3590
Abstract: This paper investigates a combination of advanced differential synthetic aperture radar interferometry (ADInSAR) with different coherence optimization methods. After the launch of satellites with polarimetry capabilities, differential synthetic aperture radar interferometry (DInSAR) is feasible to generate polarimetric DInSAR to enhance pixel phase quality and increase coherent pixel (CP) density. The first method proposed in this paper, modified coherence set-based polarimetry optimization (MCPO), is a modification of a known single-baseline coherence optimization method to optimize coherence of all interferograms simultaneously. The second method, coherence-set based polarimetry optimization (CPO), was presented by Neumann et al., and is an existing revision of the single-baseline coherence optimization technique. The final method, exhaustive search polarimetry optimization, is a search-based approach to find the optimized scattering mechanism introduced by Navarro-Sanchez et al. The case study is the Tehran basin located in the North of Iran, which suffers from a high-rate of land subsidence and is covered by agricultural fields. Usually such an area would significantly decorrelate but applying polarimetric ADInSAR allows us to obtain a more CP coverage. A set of dual polarization TerraSAR-X images with 9 × 9 and 15 × 15 as multilook factors were used within the polarimetric ADInSAR procedure. All three coherence optimization methods with two different multilook factors are shown to have increased the density and phase quality of CPs. Moreover, the estimated deformation rates were evaluated using available levelling measurements. MCPO, which is presented in this paper, works more successful than CPO in terms of CPs density, phase quality and deformation accuracy.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Ionospheric Correction of L-Band SAR Offset Measurements for the Precise
Observation of Glacier Velocity Variations on Novaya Zemlya
• Authors: Sung-Ho Chae;Won-Jin Lee;Hyung-Sup Jung;Lei Zhang;
Pages: 3591 - 3603
Abstract: The synthetic aperture radar (SAR) offset tracking method has been widely used for multitemporal analysis of fast glacier movements in the polar region. However, it can be severely distorted, particularly in the case of L-band SAR systems mainly due to a frequent occurrence of ionospheric effects in the polar region. In this study, we developed an efficient method to extract and correct the ionospheric contribution from SAR offset tracking measurements. The method exploits an iterative directional filtering approach, which is based on the pattern and directionality of ionospheric streaks. The measurement performance of the proposed method was evaluated by using three L-band advanced land observing satellite phased array type L-band synthetic aperture radar pairs. Our results showed that the proposed correction achieved the improved measurement accuracies from 4.68–23.88 to 1.03–1.51 m/yr. It means that the accuracies of corrected measurements were about 5–16 times better than those of the original measurements. From the results, we concluded that our correction technique is highly suitable for the precise measurement of the glacier displacements even in the presence of strong ionospheric effects. Using the proposed method, the variations of glacier velocities were measured in the Vylki, Shury, and Kropotnika glaciers on Novaya Zemlya, which is located in the Russian Arctic Ocean, and the grounding zones were detected from the measurements in the Shury and Kropotnika glaciers. It further confirmed that the proposed correction method is allowed for the precise monitoring of glacier movements. However, in cases of severe ionosphere-distorted measurements, the proposed method may be limitedly applied.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• POLSAR Image Classification via Wishart-AE Model or Wishart-CAE Model
• Authors: Wen Xie;Licheng Jiao;Biao Hou;Wenping Ma;Jin Zhao;Shuyin Zhang;Fang Liu;
Pages: 3604 - 3615
Abstract: Neural network such as an autoencoder (AE) and a convolutional autoencoder (CAE) have been successfully applied in image feature extraction. For the statistical distribution of polarimetric synthetic aperture radar (POLSAR) data, we combine the Wishart distance measurement into the training process of the AE and the CAE. In this paper, a new type of AE and CAE is specially defined, which we name them Wishart-AE (WAE) and Wishart-CAE (WCAE). Furthermore, we connect the WAE or the WCAE with a softmax classifier to compose a classification model for the purpose of POLSAR image classification. Compared with AE and CAE models, WAE and WCAE models can achieve higher classification accuracy because they could obtain the classification features, which are more suitable for POLSAR data. What is more, the WCAE model utilizes the local spatial information of a POLSAR image when compared with the WAE model. A convolutional natural network (CNN), which also makes use of the spatial information, has been widely applied in image classification, but our WCAE model is time-saving than the CNN model. Given the above, our methods not only improve the classification performance but also save the experimental time. Experimental results on four POLSAR datasets also demonstrate that our proposed methods are significantly effective.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• A Novel Ship Detector Based on the Generalized-Likelihood Ratio Test for
SAR Imagery
• Authors: Pasquale Iervolino;Raffaella Guida;
Pages: 3616 - 3630
Abstract: Ship detection with synthetic aperture radar (SAR) images, acquired at different working frequencies, is presented in this paper where a novel technique is proposed based on the generalized-likelihood ratio test (GLRT). Suitable electromagnetic models for both the sea clutter and the signal backscattered from the ship are considered in the new technique in order to improve the detector performance. The GLRT is compared to the traditional constant false alarm rate (CFAR) algorithm through Monte–Carlo simulations in terms of receiver operating characteristic (ROC) curves and computational load at different bands (S-, C-, and X-). Performances are also compared through simulations with different orbital and scene parameters at fixed values of band and polarization. The GLRT is then applied to real datasets acquired from different sensors (TerraSAR-X, Sentinel-1, and Airbus airborne demonstrator) operating at different bands (S-, C-, and X-). An analysis of the target-to-clutter ratio (TCR) is then performed and detection outcomes are compared with an automatic identification system data when available. Simulations show that the GLRT presents better ROCs than those obtained through the CFAR algorithm. On the other side, results on real SAR images demonstrate that the proposed approach greatly improves the TCR (between 22 and 32 dB on average), but its computational time is 1.5 times slower when compared to the CFAR algorithm.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning
• Authors: Fengying Xie;Mengyun Shi;Zhenwei Shi;Jihao Yin;Danpei Zhao;
Pages: 3631 - 3640
Abstract: Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed to extract the multiscale features from each superpixel and predict the superpixel as one of three classes including thick cloud, thin cloud, and noncloud. Finally, the predictions of all the superpixels in the image yield the cloud detection result. In the proposed cloud detection framework, the improved SLIC method can obtain accurate cloud boundaries by optimizing initial cluster centers, designing dynamic distance measure, and expanding search space. Moreover, different from traditional cloud detection methods that cannot achieve multilevel detection of cloud, the designed deep CNN model can not only detect cloud but also distinguish thin cloud from thick cloud. Experimental results indicate that the proposed method can detect cloud with higher accuracy and robustness than compared methods.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Adaptive Scale Selection for Multiscale Segmentation of Satellite Images
• Authors: Ya'nan Zhou;Jun Li;Li Feng;Xin Zhang;Xiaodong Hu;
Pages: 3641 - 3651
Abstract: With dramatically increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. Multiscale image segmentation is a prerequisite step that splits an image into hierarchical homogeneous segmented objects for OBIA. However, scale selection remains a challenge in multiscale segmentation. In this study, we presented an adaptive approach for defining and estimating the optimal scale in the multiscale segmentation process. Central to our method is the combined use of image features from segmented objects and prior knowledge from historical thematic maps in a top-down segmentation procedure. Specifically, the whole image was first split into segmented objects, with the largest scale in a presupposition segmentation scale sequence. Second, based on segmented object features and prior knowledge in the local region of thematic maps, we calculated complexity values for each segmented object. Third, if the complexity values of an object were large enough, this object would be further split into multiple segmented objects with a smaller scale in the scale sequence. Then, in the similar manner, complex segmented objects were split into the simplest objects iteratively. Finally, the final segmentation result was obtained and evaluated. We have applied this method on a GF-1 multispectral satellite image and a ZY-3 multispectral satellite image to produce multiscale segmentation maps and further classification maps, compared with the state-of-the-art and the traditional mean shift algorithm. The experimental results illustrate that the proposed method is practically helpful and efficient to produce the appropriate segmented image objects with optimal scales.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled
Region-Based Convolutional Neural Networks
• Authors: Zhipeng Deng;Hao Sun;Shilin Zhou;Juanping Zhao;Huanxin Zou;
Pages: 3652 - 3664
Abstract: Vehicle detection in aerial images, being an interesting but challenging problem, plays an important role for a wide range of applications. Traditional methods are based on sliding-window search and handcrafted or shallow-learning-based features with heavy computational costs and limited representation power. Recently, deep learning algorithms, especially region-based convolutional neural networks (R-CNNs), have achieved state-of-the-art detection performance in computer vision. However, several challenges limit the applications of R-CNNs in vehicle detection from aerial images: 1) vehicles in large-scale aerial images are relatively small in size, and R-CNNs have poor localization performance with small objects; 2) R-CNNs are particularly designed for detecting the bounding box of the targets without extracting attributes; 3) manual annotation is generally expensive and the available manual annotation of vehicles for training R-CNNs are not sufficient in number. To address these problems, this paper proposes a fast and accurate vehicle detection framework. On one hand, to accurately extract vehicle-like targets, we developed an accurate-vehicle-proposal-network (AVPN) based on hyper feature map which combines hierarchical feature maps that are more accurate for small object detection. On the other hand, we propose a coupled R-CNN method, which combines an AVPN and a vehicle attribute learning network to extract the vehicle's location and attributes simultaneously. For original large-scale aerial images with limited manual annotations, we use cropped image blocks for training with data augmentation to avoid overfitting. Comprehensive evaluations on the public Munich vehicle dataset and the collected vehicle dataset demonstrate the accuracy and effectiveness of the proposed method.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of
Satellite Images
• Authors: Shilpa Suresh;Shyam Lal;Chintala Sudhakar Reddy;Mustafa Servet Kiran;
Pages: 3665 - 3676
Abstract: Owing to the increased demand for satellite images for various practical applications, the use of proper enhancement methods are inevitable. Visual enhancement of such images mainly focuses on improving the contrast of the scene procured, conserving its naturalness with minimum image artifacts. Last one decade traced an extensive use of metaheuristic approaches for automatic image enhancement processes. In this paper, a robust and novel adaptive Cuckoo search based Enhancement algorithm is proposed for the enhancement of various satellite images. The proposed algorithm includes a chaotic initialization phase, an adaptive $\mathbf L\acute{e}vy$ flight strategy and a mutative randomization phase. Performance evaluation is done by quantitative and qualitative results comparison of the proposed algorithm with other state-of-the-art metaheuristic algorithms. Box-and-whisker plots are also included for evaluating the stability and convergence capability of all the algorithms tested. Test results substantiate the efficiency and robustness of the proposed algorithm in enhancing a wide range of satellite images.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Smooth and Sparse Regularization for NMF Hyperspectral Unmixing
• Authors: Yaser Esmaeili Salehani;Saeed Gazor;
Pages: 3677 - 3692
Abstract: In this paper, we propose a matrix factorization method for hyperspectral unmixing using the linear mixing model. In this method, we add the $\arctan$ functions of the endmembers to the $\ell _2$-norm of the error in order to exploit the sparse property of the fractional abundances. Most of the energy of spectral signatures of materials is concentrated around the first few subbands resulting in smooth spectral signatures. To exploit this smoothness, we also add a weighted norm of the spectral signatures of the materials and to limit their nonsmooth errors. We propose a multiplicative iterative algorithm to solve this minimization problem as a nonnegative matrix factorization (NMF) problem. We apply our proposed Arctan-NMF method on the synthetic data from real spectral library and compare the performance of Arctan-NMF method with several state-of-the-art unmixing methods. Moreover, we evaluate the efficiency of Arctan-NMF on two different types of real hyperspectral data. Our simulations show that the Arctan-NMF is more effective than the state-of-the-art methods in terms of spectral angle distance and abundance angle distance.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Constrained Nonnegative Matrix Factorization Based on Particle Swarm
Optimization for Hyperspectral Unmixing
• Authors: Bin Yang;Wenfei Luo;Bin Wang;
Pages: 3693 - 3710
Abstract: Spectral unmixing is an important part of hyperspectral image processing. In recent years, constrained nonnegative matrix factorization (CNMF) has been successfully applied for unmixing without the pure-pixel assumption and the result is physically meaningful. However, traditional CNMF algorithms always have two limitations: 1) Most of them are based on gradient methods and usually get trapped in a local optimum. 2) As they adopt static penalty function as the constraint handling method, it's difficult to choose a proper regularization parameter that can balance the tradeoff between reconstruction error and constraint well, which leads to the decreased accuracy. In this paper, we introduce particle swarm optimization (PSO) combined with two types of progressive constraint handling approaches for spectral unmixing in the framework of CNMF. A basic method called high-dimensional double-swarm PSO (HDPSO) algorithm is first proposed. It divides the original high-dimension problem into a series of easier subproblems and adopts two interactive swarms to search endmembers and abundances, respectively. Then, adaptive PSO (APSO) and multiobjective PSO algorithms are proposed by respectively incorporating adaptive penalty function and multiobjective optimization approaches into HDPSO. Experiments with both simulated data and real hyperspectral images are used to compare these methods with traditional algorithms and results validate that the proposed methods give better performance for spectral unmixing.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Spectral Unmixing-Based Clustering of High-Spatial Resolution
Hyperspectral Imagery
• Authors: Eleftheria A. Mylona;Olga A. Sykioti;Konstantinos D. Koutroumbas;Athanasios A. Rontogiannis;
Pages: 3711 - 3721
Abstract: This paper introduces a novel unsupervised spectral unmixing-based clustering method for high-spatial resolution hyperspectral images (HSIs). In contrast to most clustering methods reported so far, which are applied on the spectral signature representations of the image pixels, the idea in the proposed method is to apply clustering on the abundance representations of the pixels. Specifically, the proposed method comprises two main processing stages namely: an unmixing stage (consisting of the endmember extraction and abundance estimation (AE) substages) and a clustering stage. In the former stage, suitable endmembers are selected first as the most representative pure pixels. Then, the spectral signature of each pixel is expressed as a linear combination of the endmembers’ spectral signatures and the pixel itself is represented by the relative abundance vector, which is estimated via an efficient AE algorithm. The resulting abundance vectors associated with the HSI pixels are next fed to the clustering stage. Eventually, the pixels are grouped into clusters, in terms of their associated abundance vectors and not their spectral signatures. Experiments are performed on a synthetic HSI dataset as well as on three airborne HSI datasets of high-spatial resolution containing vegetation and urban areas. The experimental results corroborate the effectiveness of the proposed method and demonstrate that it outperforms state-of-the-art clustering techniques in terms of overall accuracy, average accuracy, and kappa coefficient.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Higher Order Nonlinear Hyperspectral Unmixing for Mineralogical Analysis
Over Extraterrestrial Bodies
• Authors: Andrea Marinoni;Harold Clenet;
Pages: 3722 - 3733
Abstract: Algorithms allowing the deconvolution of hyperspectral data play a key-role in remotely sensed data processing for mineralogical investigation. Modified Gaussian model (MGM) based methods are of particular interest because they are able to retrieve accurate estimates of minerals abundances and chemistry in surface's rocks. However, MGM-based frameworks deliver high computational complexity and sensitivity to initial parameters for statistical distribution definition. In this paper, a new approach for efficient and robust mineralogical investigation over extraterrestrial bodies is introduced. The proposed framework takes advantage of the solid characterization of remote sensing hyperspectral images by unmixing higher order nonlinear combinations of reflectance features associated with mafic minerals. Experimental results achieved over Mars and Moon hyperspectral images show that the proposed scheme is able to retrieve magmatic mineral abundance maps that are highly correlated to those achieved by means of MGM-based scheme while overcoming the aforesaid issues. Finally, an empirical study allowing to distinguish between clino- and orthopyroxenes by properly processing the outcomes of nonlinear hyperspectral unmixing method is reported.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• A Novel Multiple Kernel Learning Framework for Multiple Feature
Classification
• Authors: Saeid Niazmardi;Abdolreza Safari;Saeid Homayouni;
Pages: 3734 - 3743
Abstract: Multiple Kernel Learning (MKL) algorithms have recently demonstrated their effectiveness for classifying the data with numerous features. These algorithms aim at learning an optimal composite kernel through combining the basis kernels constructed from different features. Despite their satisfactory results, MKL algorithms assume that the basis kernels are a priori computed. Moreover, they adopt complex optimization methods to train the combination of the basis kernels, which are usually hard to solve and can only handle the binary classification problems. In this paper, a novel MKL framework was introduced in order to address all these issues. This framework optimizes a data-dependent kernel evaluation measure in order to learn both the basis kernels and their combination. The kernel evaluation measure should be able to estimate the goodness of the composite kernel for a multiclass classification problem. In this paper, we defined such a measure based on the similarity between the composite kernel and an ideal kernel. To this end, three different kernel-based similarity measures, namely kernel alignment (KA), centered kernel alignment (CKA), and Hilbert-Schmidt independence criterion (HSIC), were presented. For solving the optimization problem of the proposed MKL framework, we used the metaheuristic optimization algorithms, which in addition to being accurate algorithms can be easily implemented. The performance of the proposed framework was evaluated by classifying the features extracted from two multispectral and hyperspectral datasets. The results showed that this framework outperformed the other state-of-the-art MKL algorithms in terms of both classification accuracy and the computational time.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Estimation of Land Surface Temperature Using FengYun-2E (FY-2E) Data: A
Case Study of the Source Area of the Yellow River
• Authors: Xiaoning Song;Yawei Wang;Bohui Tang;Pei Leng;Sun Chuan;Jian Peng;Alexander Loew;
Pages: 3744 - 3751
Abstract: Land surface temperature (LST) is a key variable used for studies of water cycles and energy budgets of land–atmosphere interfaces. This study addresses the theory of LST retrieval from data acquired by the Chinese operational geostationary meteorological satellite FengYun-2E (FY-2E) in two thermal infrared channels (IR1: 10.29–11.45 μm and IR2: 11.59–12.79 μm) using a generalized split-window algorithm. Specifically, land surface emissivity (LSE) in the two thermal infrared channels is estimated from the LSE in channels 31 and 32 of the moderate-resolution imaging spectroradiometer (MODIS) product. In addition, an eight-day composition MODIS LSE product (MOD11A2) and the daily MODIS LSE product (MOD11A1) are used in the algorithm to estimate FY-2E emissivities. The results indicate that the LST derived from MOD11A1 is more accurate and, therefore, more appropriate for daily cloud-free LST estimation. Finally, the estimated LST was validated using the MODIS LST product for the heterogeneous source area of the Yellow River. The results show a significant correlation between the two datasets, with a correlation coefficient (R) varying from 0.60 to 0.94 and a root mean square error ranging from 1.89 to 3.71 K. Moreover, the estimated LST agrees well with ground-measured soil temperatures, with an R of 0.98.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors
for Single-Frame Small Target Detection
• Authors: Yimian Dai;Yiquan Wu;
Pages: 3752 - 3767
Abstract: Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and nonlocal priors simultaneously. First, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background nonlocal self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entrywise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the elementwise reweighted higher order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow
Mixed Remote Sensed Scene
• Authors: Renbo Luo;Wenzhi Liao;Hongyan Zhang;Liangpei Zhang;Paul Scheunders;Youguo Pi;Wilfried Philips;
Pages: 3768 - 3781
Abstract: Recent advances in sensor design allow us to gather more useful information about the Earth's surface. Examples are hyperspectral (HS) and Light Detection And Ranging (LiDAR) sensors. These, however, have limitations. HS data cannot distinguish different objects made from similar materials and highly suffers from cloud-shadow regions, whereas LiDAR cannot separate distinct objects that are at the same altitude. For an increased classification performance, fusion of HS and LiDAR data recently attracted interest but remains challenging. In particular, these methods suffer from a poor performance in cloud-shadow regions because of the lack of correspondence with shadow-free regions and insufficient training data. In this paper, we propose a new framework to fuse HS and LiDAR data for the classification of remote sensing scenes mixed with cloud-shadow. We process the cloud-shadow and shadow-free regions separately, our main contribution is the development of a novel method to generate reliable training samples in the cloud-shadow regions. Classification is performed separately in the shadow-free (classifier is trained by the available training samples) and cloud-shadow regions (classifier is trained by our generated training samples) by integrating spectral (i.e., original HS image), spatial (morphological features computed on HS image) and elevation (morphological features computed on LiDAR) features. The final classification map is obtained by fusing the results of the shadow-free and cloud-shadow regions. Experimental results on a real HS and LiDAR dataset demonstrate the effectiveness of the proposed method, as the proposed framework improves the overall classification accuracy with 4% for whole scene and 10% for shadow-free regions over the other methods.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Single-Pass Sub-Meter Space-Based GNSS-R Ice Altimetry: Results From TDS-1
• Authors: Changjiang Hu;Craig Benson;Chris Rizos;Li Qiao;
Pages: 3782 - 3788
Abstract: Space-based Global Navigation Satellite System Reflectometry (GNSS-R) altimetry remains an open challenge. This paper reports on space-based GNSS-R altimetry using 40-s period of intermediate frequency recording from the TechDemoSat-1 mission. This recording is unique because one GPS signal is reflected from ice. The waveforms that are used to determine path delay are generated by 1 ms coherent integration. Pseudoranges are smoothed every 0.5 s by linear models before calculating the path delay. Altimetric results are compared to DTU10 mean sea surface heights, with good agreement being obtained. The RMS difference of 4.4 m is much smaller than reported in the current literature. Very good altimetric precision of better than 1 m (0.96 m) is achieved with a spatial resolution of 3.8 km. This result validates the potential of space-based GNSS-R altimetry.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Neural Networks Based Sea Ice Detection and Concentration Retrieval From
GNSS-R Delay-Doppler Maps
• Authors: Qingyun Yan;Weimin Huang;Cecilia Moloney;
Pages: 3789 - 3798
Abstract: In this paper, a neural networks (NN) based scheme is presented for detecting sea ice and retrieving sea ice concentration (SIC) from global navigation satellite system reflectometry delay-Doppler maps (DDMs). Here, a multilayer perceptron neural network with back-propagation learning is adopted. In practice, two NN were separately developed for sea ice detection and concentration retrieval purposes. In the training phase, DDM pixels were employed as an input. The SIC data obtained by Nimbus-7 SMMR and DMSP SSM/I-SSMIS sensors were used as the target data, which were also regarded as ground-truth data in this paper. After the training process using a dataset collected around February 4, 2015, these networks were used to produce corresponding detection and concentration estimation for other four sets of DDM data, which were collected around February 12, 2015, February 20, 2015, March 16, 2015, and April 17, 2015, respectively. Results show high accuracy in sea ice detection and concentration estimation with DDMs using the proposed scheme. On average, the accuracy for sea ice detection is about 98.4%. In terms of estimated SIC, the mean absolute error is less than 9%, whereas the correlation coefficient is as high as 0.93 compared with the reference data. It was also found that low sea state and wind speed could lead to an overestimation of SIC for partially ice-covered region.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Retrieving Hurricane Maximum Winds Using Simulated CYGNSS
Power-Versus-Delay Waveforms
• Authors: Faozi Saïd;Stephen John Katzberg;Seubson Soisuvarn;
Pages: 3799 - 3809
Abstract: A novel approach in retrieving hurricane maximum winds using simulated NASA Cyclone Navigation Satellite System (CYGNSS) data is presented. Five hundred fifty two hurricane wind fields, from the 2010–2011 Atlantic and Eastern pacific hurricane seasons, were used to test the algorithm. These wind fields have been obtained from the hurricane weather research and forecasting model (HWRF). Power-versus-delay waveforms associated with specular points located along CYGNSS tracks crossing these wind fields were simulated. These “storm” power-versus-delay waveforms were compared to “reference” power-versus-delay waveforms generated over a set of synthetic Willoughby storms with known maximum wind speeds. The retrieved maximum wind speeds are compared against the hurricane research division reanalysis data (Best Track) and HWRF. For Best Track maximum wind speeds less than 40 m/s and greater than 40 m/s, the overall bias against Best Track is 11.3 and 2.1 m/s, respectively. When comparing against HWRF maximum wind speeds less than 40 m/s and greater than 40 m/s, the overall bias is 11.5 and 3.0 m/s, respectively. These results are improved when translation effects were applied to these synthetic storms: compared against Best Track for maximum wind speeds less than 40 m/s and greater than 40 m/s, the biases are 9.0 and $-$1.13 m/s, respectively. When compared against HWRF, the biases are 8.6 and 0.4 m/s, respectively.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Wavelet-Based Higher Order Correlative Stacking for Seismic Data Denoising
in the Curvelet Domain
• Authors: Jing-He Li;Yu-Jie Zhang;Rui Qi;Qing Huo Liu;
Pages: 3810 - 3820
Abstract: To whiten random noise and identify coherent noise while preserving the features of seismic events, a hybrid denoising scheme of wavelet-based higher order correlative stacking (HOCS) in the curvelet domain is proposed. The proposed algorithm uses HOCS to isolate the coefficients of seismic events in the curvelet domain. It then removes the noises and recovers signals recorded in noisy environment, without the need to choose an arbitrary threshold; the HOCS method selects a threshold automatically in the curvelet domain. Therefore, with the HOCS, it is possible to capture the features of useful signals with good correlations at all scales and all angles, then to remove the features of coherent noise with disordered correlations. Using interpretive seismic records of karst cavities and hidden sinkhole detections after artificial backfill, we show that the proposed scheme improves noisy seismic data significantly with respect to both signal-to-noise ratio and fidelity. To demonstrate the advantages of this hybrid denoising scheme, a comparison of the performances between different individual denoising methods is investigated for complex seismic records contaminated with different types of noise. Numerical case studies and three field data examples validate the effectiveness of the hybrid denoising scheme proposed in this paper.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Matching Pursuit-Based Sliced Wigner Higher Order Spectral Analysis for
Seismic Signals
• Authors: Yuqing Wang;Zhenming Peng;Xiaoyang Wang;Yanmin He;
Pages: 3821 - 3828
Abstract: The Wigner higher order spectra (WHOS) are multidimensional time–frequency distributions defined by extending the Wigner–Ville distribution (WVD) to higher order spectra domains. As a subset of WHOS, the sliced WHOS (SWHOS) are used for conveniently representing time–frequency spectra. The SWHOS provide a better localized time–frequency support compared with WVD, but still suffers from cross term issues. Therefore, we propose a matching pursuit-based sliced Wigner higher order spectra (MP-SWHOS) algorithm, which can obtain a sparser high-resolution time–frequency spectrum without cross terms. The performance of MP-SWHOS is assessed on a simulated model and real data. The application to seismic spectral decomposition shows that the proposed algorithm can provide single-frequency slices with greater precision, important in the analysis of hydrocarbon reservoirs.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Introducing IEEE collabratec
• Pages: 3831 - 3831
Abstract: Advertisement, IEEE. IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical interests, or career pursuits. You can also create and share a professional identity that showcases key accomplishments and participate in groups focused around mutual interests, actively learning from and contributing to knowledgeable communities. All in one place! Learn about IEEE Collabratec at ieeecollabratec.org.
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

• Proceedings of the IEEE
• Pages: 3832 - 3832
PubDate: Aug. 2017
Issue No: Vol. 10, No. 8 (2017)

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