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  Subjects -> ELECTRONICS (Total: 156 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 4)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 6)
Advances in Microelectronic Engineering     Open Access   (Followers: 9)
Advances in Power Electronics     Open Access   (Followers: 16)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 139)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 18)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 11)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 6)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 24)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 15)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 24)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 9)
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: 34)
China Communications     Full-text available via subscription   (Followers: 6)
Circuits and Systems     Open Access   (Followers: 12)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 76)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 67)
Electronic Markets     Hybrid Journal   (Followers: 6)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 49)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 46)
Electronics Letters     Hybrid Journal   (Followers: 22)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 38)
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: 11)
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   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 58)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 43)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 34)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 23)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 7)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 37)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 34)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 45)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 13)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 25)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 11)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 16)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 43)
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: 13)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 11)
IET Power Electronics     Hybrid Journal   (Followers: 22)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 14)
IETE Journal of Education     Open Access   (Followers: 3)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 6)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 25)
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: 7)
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: 5)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 16)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
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: 14)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 10)
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: 3)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 6)
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: 12)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 7)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
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: 2)
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: 13)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 6)
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: 3)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 100)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 6)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 8)
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: 7)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Sensors     Open Access   (Followers: 17)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Infotel     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 5)
Metrology and Measurement Systems     Open Access   (Followers: 4)
Microelectronics and Solid State Electronics     Open Access   (Followers: 13)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 31)
Nanotechnology, Science and Applications     Open Access   (Followers: 3)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Open Journal of Antennas and Propagation     Open Access   (Followers: 3)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 11)
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: 3)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 41)
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: 51)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 9)
Solid-State Electronics     Hybrid Journal   (Followers: 6)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 1)
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: 5)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 5)
Wireless Power Transfer     Full-text available via subscription   (Followers: 3)
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]   [41 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • Institutional listings
    • Abstract: Advertisement.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Information for Authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Table of Contents
    • Pages: 2837 - 2839
      Abstract: Presents the table of contents for this issue of the publication.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Foreword to the Special Issue on “GeoVision: Computer Vision for
           Geospatial Applications”
    • Pages: 2840 - 2843
      Abstract: The nine papers in this special section focus on the development of new computer vision techniques for the interpretation of remote sensing images. These papers represent a follow-up of two workshops held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015, that was held in Boston, MA, EARTHVISION 2015 and MSF 2015. The purpose of both workshops and of this special issue is to foster fruitful collaboration of computer vision, Earth observation, and geospatial analysis communities.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Immersive Interactive SAR Image Representation Using Non-negative Matrix
    • Pages: 2844 - 2853
      Abstract: Earth observation (EO) images clustering is a challenging problem in data mining, where each image is represented by a high-dimensional feature vector. However, the feature vectors might not be appropriate to express the semantic content of images, which eventually lead to poor results in clustering and classification. To tackle this problem, we propose an interactive approach to generate compact and informative features from images content. To this end, we utilize a 3-D interactive application to support user-images interactions. These interactions are used in the context of two novel nonnegative matrix factorization (NMF) algorithms to generate new features. We assess the quality of new features by applying k-means clustering on the generated features and compare the obtained clustering results with those achieved by original features. We perform experiments on a synthetic aperture radar (SAR) image dataset represented by different state-of-the-art features and demonstrate the effectiveness of the proposed method. Moreover, we propose a divide-and-conquer approach to cluster a massive amount of images using a small subset of interactions.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Multiclass Labeling of Very High-Resolution Remote Sensing Imagery by
           Enforcing Nonlocal Shared Constraints in Multilevel Conditional Random
           Fields Model
    • Authors: Zhang; T.;Yan, W.;Li, J.;Chen, J.;
      Pages: 2854 - 2867
      Abstract: In this study, we investigate the problem of multiclass pixel labeling of very high-resolution (VHR) optical remote sensing images. We propose a novel higher order potential function based on nonlocal shared constraints within the framework of a three-level conditional random field (CRF) model. The proposed approach combines classification knowledge discovery from labeled data with unsupervised segmentation cues derived from the cosegmentation of test data. The cosegmentation of unannotated test data incorporates nonlocal constraints, which are encoded in a novel truncated robust consistency potential function. The class labels are then updated iteratively by alternating between estimating semantic segmentations using CRF and integrating cosegmentation-derived labels in higher order potential functions to refine labeling results. We experimentally demonstrate the improved labeling accuracy of our approach compared with state-of-the-art multilevel CRF approaches based on quantitative and qualitative results. We also show that our approach can address the issue of lacking accurately labeled training data.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Semantic Labeling of Aerial and Satellite Imagery
    • Pages: 2868 - 2881
      Abstract: Inspired by the recent success of deep convolutional neural networks (CNNs) and feature aggregation in the field of computer vision and machine learning, we propose an effective approach to semantic pixel labeling of aerial and satellite imagery using both CNN features and hand-crafted features. Both CNN and hand-crafted features are applied to dense image patches to produce per-pixel class probabilities. Conditional random fields (CRFs) are applied as a postprocessing step. The CRF infers a labeling that smooths regions while respecting the edges present in the imagery. The combination of these factors leads to a semantic labeling framework which outperforms all existing algorithms on the International Society of Photogrammetry and Remote Sensing (ISPRS) two-dimensional Semantic Labeling Challenge dataset. We advance state-of-the-art results by improving the overall accuracy to $88%$ on the ISPRS Semantic Labeling Contest. In this paper, we also explore the possibility of applying the proposed framework to other types of data. Our experimental results demonstrate the generalization capability of our approach and its ability to produce accurate results.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Fully Connected Continuous Conditional Random Field With Stochastic
           Cliques for Dark-Spot Detection In SAR Imagery
    • Pages: 2882 - 2890
      Abstract: Dark-spot detection from synthetic aperture radar (SAR) imagery is a fundamental step in marine oil-spill detection and monitoring. However, achieving robust and accurate detection is difficult due to SAR sensor limitations and the complex marine environment. To address this problem, the large-scale spatial contextual information in SAR imagery has to be utilized to increase the class separability between the dark spot and the background. A stochastic fully connected continuous conditional random field (SFCCRF) approach to model SAR imagery and perform soft-label inference has been designed and built, leading to an efficient detection algorithm. Instead of treating all pixels in the imagery as being connected, SFCCRF determines the connectivity of two pixels in a stochastic manner based on their proximity in both feature space and image space. Since SFCCRF provides an efficient and effective way for modeling the large-scale spatial correlation effect, the resulting soft labels can resist the influence of speckle noise and highlight the difference between dark spot and the background. Dark-spot detection is achieved by binarizing the soft labels estimated by SFCCRF. The proposed algorithm is tested on both simulated and real SAR imageries. The results show that SFCCRF can delineate the dark spot with low commission and omission error rates.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Saliency Detector for SAR Images Based on Pattern Recurrence
    • Authors: Wang; H.;Xu, F.;Chen, S.;
      Pages: 2891 - 2900
      Abstract: In order to detect targets from nonuniform background in synthetic aperture radar (SAR) images, this paper proposes a new approach for saliency detection based on the idea of pattern recurrence. The pattern recurrence quantifies the saliency of a local patch by how well it can be reconstructed from patches in the background. The similarity between polarimetric SAR (PolSAR) patches is defined as the likelihood that both patches belong to the same Wishart distribution. A simple saliency indicator is defined as the normalized variance of the nonlocal similarity map. To detect target of different sizes, multiscale patches are also used. The proposed approach is first tested on optical images and results are compared with other saliency detection method and experiments of eye fixation prediction. Test results on real SAR images show that the proposed method has robust performance on target detection with and without the presence complicated background.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Transfer Sparse Subspace Analysis for Unsupervised Cross-View Scene Model
    • Authors: Sun; H.;Liu, S.;Zhou, S.;Zou, H.;
      Pages: 2901 - 2909
      Abstract: With the availability of an increasing amount of images from the Internet and several user-friendly crowd sourcing tools such as Amazon Mechanical Turk (AMT), many large-scale ground level image datasets with semantic annotations have been collected in the vision community, and they have fostered many efficient ways to describe the image content. For example, visual attributes have shown promising potentials in visual recognition and retrieval. However, the scarcity of labeled samples in the earth observation (EO) community (collection of labeled samples through photointerpretation or terrestrial campaigns is time consuming and expensive, and often requires expertise background) hinders the semantic understanding of remote sensing images. In this paper, we propose to transfer the semantic knowledge learned from ground view scene images to overhead view very high-resolution (VHR) remote sensing images. Specifically, a novel transfer sparse subspace analysis (TSSA) algorithm is presented for unsupervised cross-view scene modeladaptation. TSSA aims at finding a common embedding of the data across different views by simultaneously 1) minimizing the maximum mean discrepancy (MMD), 2) preserving the main statistical property, and 3) maintaining the self-expressiveness property of the data in a reproducing kernel Hilbert space (RKHS). Two variants of cross-view scene knowledge transfer have been investigated in our experiments. The first one is transfer of scene category models, and the second is transfer of scene attributes models. Experimental results demonstrate the competitive performance of our method with respect to state of the art.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Combining Active and Semisupervised Learning of Remote Sensing Data Within
           a Renyi Entropy Regularization Framework
    • Authors: Polewski; P.;Yao, W.;Heurich, M.;Krzystek, P.;Stilla, U.;
      Pages: 2910 - 2922
      Abstract: Active and semisupervised learning are related techniques aiming at reducing the effort of creating training sets for classification and regression tasks. In this work, we present a framework for combining these two techniques on the basis of Renyi entropy regularization, enabling a synergy effect. We build upon the existing semisupervised learning model which attempts to balance the likelihood of labeled examples and the entropy of putative object probabilities within the unlabeled pool. To enable efficient optimization of the model, we generalize the deterministic annealing expectation–maximization (DAEM) algorithm, originally designed for Shannon entropy, to accommodate the use of Renyi entropies. The Renyi-regularized model is then applied to expected error reduction (EER), an active learning approach based on minimizing the entropy of unlabeled object probabilities. We investigate object preselection with a greedy approximation of the object feature matrix as a means to reduce computational complexity. To assess the performance of the proposed framework, we apply it to two real-world remote sensing problems with significantly different input data characteristics: detecting dead trees from color infrared aerial images (2-D) and detecting dead trunk stems in ALS point clouds (3-D). Our results show that for small training sets, the semisupervised Renyi-regularized classifier improves the classification rate by up to 11% and 10% points compared to the unregularized baseline for ALS and image data, respectively. This gain carries over to active learning, where the regularized EER achieves 90% of the final classification performance using 50% and 70% of the number of queries required by standard EER.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Adaptation and Evaluation of an Optical Flow Method Applied to
           Coregistration of Forest Remote Sensing Images
    • Pages: 2923 - 2939
      Abstract: The coregistration of heterogeneous geospatial images is useful in various remote sensing applications. Since the number of available data increases and the resolution improves, it is interesting to have an approach as automated, fast, robust, and accurate as possible. In this paper, we present a solution based on optical-flow computation. This algorithm called GeFolki allows the registration of images in a nonparametric and dense way. GeFolki is based on a local method of optical flow derived from the Lucas–Kanade algorithm, with a multiscale implementation, and a specific filtering including rank filtering, rolling guidance filtering and local contrast inversion. The efficiency of our coregistration chain is shown on radar, LIDAR, and optical images on Remningstorp forest in Sweden. An analysis of the relevant parameters is investigated for several scenarios. Finally, we demonstrate the accuracy of our coregistration by proposing specific metrics for LIDAR/radar coregistration, and optics/radar coregistration.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Graph-Based Registration, Change Detection, and Classification in Very
           High Resolution Multitemporal Remote Sensing Data
    • Pages: 2940 - 2951
      Abstract: In this paper, we propose a modular, scalable, metric-free, single-shot change detection/registration method. The developed framework exploits the relation between the registration and change detection problems, while under a fruitful synergy, the coupling energy term constrains adequately both tasks. In particular, through a decomposed interconnected graphical model, the registration similarity constraints are relaxed in the presence of change detection. Moreover, the deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is imposed on the deformation and the detection space as well. The proposed formulation is able to operate in a fully unsupervised manner addressing binary change detection problems, i.e., change or no-change with respect to different similarity metrics. Furthermore, the framework has been formulated to address automatically the detection of from-to change trajectories under a supervised setting. Promising results on large scale experiments demonstrate the extreme potentials of our method.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • GPR Applications Across Engineering and Geosciences Disciplines in Italy:
           A Review
    • Pages: 2952 - 2965
      Abstract: In this paper, a review of the main ground-penetrating radar (GPR) applications, technologies, and methodologies used in Italy is given. The discussion has been organized in accordance with the field of application, and the use of this technology has been contextualized with cultural and territorial peculiarities, as well as with social, economic, and infrastructure requirements, which make the Italian territory a comprehensive large-scale study case to analyze. First, an overview on the use of GPR worldwide compared to its usage in Italy over the history is provided. Subsequently, the state of the art about the main GPR activities in Italy is deepened and divided according to the field of application. Notwithstanding a slight delay in delivering recognized literature studies with respect to other forefront countries, it has been shown how the Italian contribution is now aligned with the highest world standards of research and innovation in the field of GPR. Finally, possible research perspectives on the usage of GPR in Italy are briefly discussed.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Precipitation Spectra Analysis Over China With High-Resolution
           Measurements From Optimally Merged Satellite/Gauge
           Observations—Part I: Spatial and Seasonal Analysis
    • Pages: 2966 - 2978
      Abstract: Precipitation amount (PA), frequency (PF), and intensity (PI) over China are characterized and quantified using a high-resolution merged satellite-gauge precipitation product for 6 years (January 2008 through December 2013). The precipitation product synthesizes both state-of-the-art multisatellite precipitation algorithms and the latest, densest gauge observations to provide high-quality precipitation information at a very fine temporal and spatial resolution $(text{0.1}^{circ}!/text{hourly})$ that encompasses all of China. The geographical and seasonal variations in precipitation are systematically documented over seven subregions, each corresponding to a unique climate regime. PA, PF, and PI have large seasonal and geographical variations across China. It is found that 1) although heavy precipitation events $({ > } 10 ;text{mm/h})$ represent only 0.8% of total precipitation occurrence over China, they contribute 12.1% of the total precipitation volume. Light precipitation events $({ < } 1 ;text{mm/h})$ dominate the precipitation occurrence (74.3%) and contribute 23.1% of the total precipitation volume; 2) over the high-altitude Tibetan Plateau (TP), the landlocked Xinjiang (XJ) province, and northwestern China (NW), light precipitation events $({ < } 1 ;text{mm/h})$ occur very frequently (74.7%, 82.1%, and 64.1% of all precipitation events) and contribute 29.8%, 35.5%, and 27.4% of the total precipitation volume. This initial continental-scale study provides new insights on precipitation characteristics that can benefit meteorological and hydrological modeling and applications, especially in areas with sparse rain-gauge coverage.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Precipitation Spectra Analysis Over China With High-Resolution
           Measurements From Optimally-Merged Satellite/Gauge
           Observations—Part II: Diurnal Variability Analysis
    • Pages: 2979 - 2988
      Abstract: Timing and diurnal variation of summer precipitation is analyzed over China using a new high-resolution ( $0.1^circ,text{hourly}$ ) satellite-gauge merged surface rainfall dataset that spans from 2008 through 2013. The results show that: 1) both precipitation amount (PA) and frequency (PF) show strong diurnal cycles with local solar time (LST); 2) peak times of PA (PAPT) primarily occur from 15 LST to 00 LST in most parts of the Tibet Plateau (TP), Xinjiang (XJ), Northwestern China (NW), Northeastern China (NE), and Southern China (SC), and the PAPT occurs from 00 LST to 09 LST in southern TP, Eastern XJ, western NW, southern NE, eastern Northern China (NC), and most parts of Southwestern China (SW); 3) the PAPT transitions eastward with time, occurring at ${sim}15; text{LST}$ in central TP, at midnight in SW, and at 15–18 LST in the eastern coastal regions that are in the lower reach of Yangtze River and in the north side of Wuyi Mountains; 4) peak times of PF (PFPT) show a similar spatial pattern with PAPT, but with a small temporal (1–2 h) lead; 5) peak times of precipitation intensity (PIPT) occur during the 18–00 LST time frame in the southeastern TP and central SW regions. The PIPT along the upper Yangtze River valley occurs around 00–06 LST. The PIPT occurs in the morning at around 06–09 LST in the mid-lower Yangtze River valley and most parts of SC. This study on the diurnal cycle of precipitation over China can be used as a reference to validate atmospheric and hydrologic models, and also to guide hydrometerological research and applications.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • A Novel Doppler Frequency Model and Imaging Procedure Analysis for
           Bistatic ISAR Configuration With Shorebase Transmitter and Shipborne
    • Pages: 2989 - 3000
      Abstract: In order to avoid bad bistatic geometry cases and offer better concealment and anti-interference ability, a novel kind of bistatic inverse synthetic aperture radar (ISAR) configuration, named shorebase–shipborne bistatic ISAR, is presented. In this paper, the Doppler frequency model of the received signal for shorebase–shipborne bistatic ISAR is proposed, when both the target and the receiver ship have nonstationary three-dimensional (3-D) rotational motion. An analysis of the Doppler shift and image aliasing induced by the receiver ship’s rotational motion is given. To eliminate the effects of receiver ship’s rotational motion, a new image reconstruction method in combination with range-instantaneous Doppler (RID) technique and rotational motion compensation is proposed. The simulated results demonstrate the effects of the receiver ship’s rotational motion and the effectiveness of new image reconstruction method.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Semisupervised Feature Extraction With Neighborhood Constraints for
           Polarimetric SAR Classification
    • Pages: 3001 - 3015
      Abstract: The supervised feature extraction methods have a relative high performance since the discriminating information of classes is introduced from large quantities of labeled samples. However, it is labor intensive to obtain labeled samples for terrain classification. In this paper, in order to reduce the cost of labeled samples, a novel semisupervised algorithm with neighborhood constraints (SNC) is proposed for polarimetric synthetic aperture radar (PolSAR) feature extraction and terrain classification. A number of PolSAR features of each pixel and its neighbors are used to construct a spatial group, which can represent the central pixel and weaken the influence of speckle noise. Then, with the class information from a few of pixels and the neighborhood constraints, an objective function is designed for the estimation of a nonlinear low-dimensional space. Finally, the spatial groups in the original high-dimensional space are projected to this low-dimensional space, and a low-dimensional feature set is obtained. The redundancy among the features is reduced. Additionally, unlike the conventional semisupervised algorithms, because the local spatial relation of PolSAR image is utilized, the extracted features not only are discriminating but also preserve the structure of the PolSAR data, which can enhance the classification accuracy. Experiments using the extracted features for classification are performed on both the synthesized PolSAR and real PolSAR data which are from the AIRSAR, RADARSAT-2, and EMISAR. Quantitative results indicate that SNC improves the separability of features and is superior to state-of-the-art feature extraction algorithms with a few labeled pixels.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • A Joint Markov Random Field Approach for SAR Interferogram Filtering and
    • Pages: 3016 - 3025
      Abstract: This paper presents a new integrated interferometric synthetic aperture radar (InSAR) phase filtering and unwrapping method based on a Markov random field (MRF) model. This approach aims to estimate a noiseless unwrapped phase from the observed noisy interferogram. The phase image is modeled using a joint MRF with a corresponding energy function related simultaneously to noise filtering and phase unwrapping (PU). This function contains two parts: the first is for interferogram filtering process and the second aims to unwrap this filtered phase. The filtered unwrapped phase image is estimated by minimizing the proposed energy function using the genetic algorithm (GA). The proposed approach is tested and validated on simulated and real interferograms acquired with the Radarsat-2, ERS-2, and Envisat satellites acquired over two distinct regions in Tunisia (Mahdia and Ben Guerden) with different geographical characteristics. The obtained results show a significant improvement with respect to other simultaneously filtering and unwrapping algorithms.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Detection of Moving Targets by Refocusing for Airborne CSSAR
    • Pages: 3026 - 3036
      Abstract: Due to the advantage of short revisit time, airborne circular stripmap synthetic aperture radar (CSSAR) is an attractive tool for air-to-ground surveillance and reconnaissance. This paper deals with the issue of ground moving-target detection with an airborne CSSAR and proposes a new autofocus-based detection algorithm. The prominent features of the proposed algorithm are the incorporation of the Doppler ambiguity information into the autofocus-based moving-target detection and the ability to solve the problem of large range cell migration, which affects most of autofocus algorithms. The proposed algorithm can detect not only the targets whose azimuth chirp rates are different from that of the static clutter background but also the targets whose Doppler ambiguities are different from that of the static clutter background. Numerical results show that the proposed algorithm works well in both homogeneous and heterogeneous clutter backgrounds.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Inverse Omega-K Algorithm for the Electromagnetic Deception of Synthetic
           Aperture Radar
    • Pages: 3037 - 3049
      Abstract: Deceptive jamming against synthetic aperture radar (SAR) receives intensive interests during the past decade. However, it is still a challenging task to design a jamming method that is competent both in focus capability and in computational efficiency, especially in the case where jammer is confronted with SAR with significant squint angle and long synthetic aperture. In this paper, we propose an inverse omega-K algorithm and present an accurate and an approximate implementation scheme of the algorithm. The accurate scheme can achieve full focus with no regard to the squint angle and synthetic aperture of radar. Its computations include fast Fourier transform (FFT), Stolt interpolation, and complex multiply. Advantage of computational efficiency can be achieved under assumption that the support region of radar can be crudely evaluated a priori so that the most time-consuming Stolt interpolation can be done offline. The support region is determined by carrier frequency and bandwidth of radar signal, pointing direction, and azimuth beam width of radar antenna. For the case in which the support region of radar is not available to jammer beforehand, the approximate scheme is a remedy. By substituting the Stolt interpolation with Chirp-Z transform (CZT), the approximate scheme is readily fit for parallel computation and hence appealing for its high efficiency. However, the focus criterion exerts a limitation on range scale of electromagnetic deception when the squint angle of SAR is large. Both implementation schemes are verified by simulation results.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Mitigating Ionospheric Artifacts in Coseismic Interferogram Based on
           Offset Field Derived From ALOS-PALSAR Data
    • Pages: 3050 - 3059
      Abstract: Ionospheric total electron content (TEC) disturbances can seriously influence the signal of low-frequency spaceborne synthetic aperture radar (SAR) systems, e.g., Advanced Land Observation Satellite (ALOS)-phased array-type L-band synthetic aperture radar (PALSAR). With regard to coseismic studies using interferometric synthetic aperture radar (InSAR), it is vital to mitigate the ionospheric artifacts in the contaminated coseismic interferogram. In this paper, we propose a new method for the integral constant calculation, and we then aim to improve the estimation of the ionospheric phase screen (IPS). The proposed method is based on both azimuth and range displacement field maps. At present, the azimuth displacement field can be generated by an offset-tracking procedure or multiple-aperture InSAR (MAI), but the range displacement field can only be estimated by an offset-tracking procedure. We applied ALOS-PALSAR data that were acquired before and after the 2008 Wenchuan earthquake and the 2010 Darfield earthquake to test the proposed method. This case study further showed that ionospheric azimuth streaks were clearly visible in the azimuth displacement field maps of these two cases, one of which was generated using the MAI algorithm and the other using an offset-tracking algorithm. The results confirmed that the long-wavelength ionospheric artifacts in the non-coseismic regions could be corrected by the use of the proposed method. The line-of-sight (LOS) displacement corrections of these two cases, Wenchuan and Darfield, were in the range of $-35.9$ to 21.0 cm and $-6.6$ to 10.0 cm along the LOS direction, respectively.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • The Analysis and Realization of Motion Compensation for Circular Synthetic
           Aperture Radar Data
    • Pages: 3060 - 3071
      Abstract: Regarding the two-dimensional (2-D) imaging processing of circular synthetic aperture radar (CSAR), the motion compensation (MOCO) in the frequency domain is not fully developed. To perform the MOCO for CSAR data in the frequency domain, a novel three-step strategy is presented in this paper. The motion error model is established first, and it is found that a difficulty of MOCO for CSAR lies in the space variance of the motion errors. Specifically, according to the proposed MOCO strategy, this first step is space-invariant MOCO, the second step is space-variant MOCO in the range-compressed angular-wavenumber domain with respect to subaperture data, and the last step is the autofocus operation in the 2-D wavenumber domain after the polar format resampling. Eventually, the complete MOCO flowchart is obtained. Simulation and experimental tests verify that the proposed motion error model is valid and the three-step MOCO strategy can remove motion errors accurately and figure out well-focused CSAR images.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Classification of Polarimetric SAR Images Using Multilayer Autoencoders
           and Superpixels
    • Pages: 3072 - 3081
      Abstract: A new polarimetric synthetic aperture radar (PolSAR) images classification method based on multilayer autoencoders and superpixels is proposed in this paper. First, in order to explore the spatial relations between pixels in PolSAR data, the RGB image formed with Pauli decomposition is used to produce superpixels to integrate contextual information of neighborhood. Second, multilayer autoencoders network is used to learning the features used for distinguishing the multiple categories for each pixel, and a softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. Finally, the probability distributions is regarded as a new probabilistic metric and introduced to k-nearest neighbor to improve the accuracy of classification based on superpixels, which takes spatial relationship between pixels into consideration, and it is robust to speckle noise. The proposed method makes good use of the scattering characteristics in each pixel and spatial information of PolSAR data. Compared with other state-of-the-art methods, the results of proposed method show better agreement with the ground truth and significant improvement in classification accuracy and discriminability of small differences between different categories.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Rice Mapping Using RADARSAT-2 Dual- and Quad-Pol Data in a Complex
           Land-Use Watershed: Cau River Basin (Vietnam)
    • Pages: 3082 - 3096
      Abstract: The strong temporal backscatter signature of rice growing above the water's surface allows for the use of synthetic aperture radar (SAR) for paddy rice crop mapping in Southern Vietnam (Mekong Delta). In Northern Vietnam (Red River Delta), rice mapping using SAR is a challenge and is rarely performed because of the complex land-use/land-cover. Nevertheless, information about rice fields is needed for hydrological simulations in river basins such as the Cau River basin. The objective of this research is to investigate the potential of RADARSAT-2 band- C in identifying rice fields over a large and fragmented land-use area. Two methods are proposed, one for each data type, adapted to the land-use/land-cover of the study area. The thresholding technique, with a statistical analysis of the temporal variation of rice backscattering, was applied to the HH like-polarized ratio of dual-pol data. The support vector machine (SVM) algorithm was applied to the full quad-pol and a single HH-polarization calculated from polarimetric data. This study demonstrates that RADARSAT-2 dual- and quad-pol data can be successfully used to identify cultivated rice fields. However, the dual-pol data seems less efficient than the quad-pol data and the SVM classification is more flexible than the thresholding technique. Between the full quad-pol and a single polarization, the overall classification accuracy shows that the results derived from the single HH polarization are 3 to 10% less accurate than those derived from the classification of full quad-pol data. The results show the usefulness of polarimetric C-band data for the identification of rice fields in Northern Vietnam.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Classification of Local Climate Zones Using SAR and Multispectral Data in
           an Arid Environment
    • Pages: 3097 - 3105
      Abstract: There is an urgent need for more detailed spatial information on cities globally that has been acquired using a standard method to facilitate comparison and the transfer of scientific and practical knowledge between places. As part of the world urban database and access portal tools (WUDAPT) initiative, a simple workflow has been developed to perform this task. Using freely available satellite imagery (Landsat) and software (SAGA), WUDAPT characterizes settlements using the local climate zone (LCZ) scheme, which decomposes the city into distinctive neighborhoods ( ${ > } text{1} hbox{km}^2$ ) based on typical properties (e.g., green proportion and built fraction). In this paper, the methodology is extended to examine the effect of adding synthetic aperture radar (SAR) data, which is now freely available from Sentinel 1, for generating LCZs. Using the city of Khartoum as a case study, the results show that combining multispectral and SAR data improves the overall performance of several classifiers, with random forest (RF) performing the best overall.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • A Pol-SAR Analysis for Alpine Glacier Classification and Snowline Altitude
    • Pages: 3106 - 3121
      Abstract: In this study, we investigated the use of synthetic aperture radar (SAR) polarimetry (Pol-SAR) and a supervised classification technique, support vector machine (SVM), for the classification of bare soil, ice, and snow, over the Ortles–Cevedale massif, (Eastern Italian Alps). We analyzed the importance of topographic correction on the backscattering and polarimetric SAR signature and the advantage of quad-pol with respect to dual-pol data. When backscattering values only are employed, the incidence angle used as input feature of the SVM classifier assures the best classification accuracy, 9.9% higher than the accuracy obtained with cosine corrected ${gamma ^0}$ backscattering. The introduction of polarimetric features and decomposition parameters (such as Cloude–Pottier or Touzi decomposition parameters) increases the classification accuracy by 5.2% with respect to the backscattering case. The simulation of RADARSAT-2 data as Sentinel-1 like for dual-pol data shows a decrease of accuracy equal to 7.8% with respect to the fully polarimetric case (93.5%). The first Sentinel-1 image acquired on our test area was also employed for classification. We then tested the capability of C-band SAR to detect accumulation and ablation zones of the glaciers under the winter dry snow by setting up a multi-incidence angle and fully polarimetric SVM classifier, exploiting ascending and descending RADARSAT-2 data. In this case, the accuracy increased by 14.7% combining different geometric acquisitions (88.9%) with respect to the single geometry case. Finally, from the resulting classification maps, we extracted the snowline altitude for a sample of three glaciers, using both optical and SAR data, comparing the different products.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Interpretation of In Situ Observations in Support of
           P-Band Radar Retrievals
    • Pages: 3122 - 3130
      Abstract: Current estimates of Net Ecosystem Exchange (NEE) at regional and continental scales contain significant uncertainty. It has been shown that root-zone soil moisture (RZSM) content has a first-order effect on NEE. The objective of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project is to provide measurements to estimate RZSM using a P-band airborne radar. Flight operations are conducted over representative sites of the nine major North American biomes which have been instrumented with soil water content sensors in various locations and over various depth layers which will be correlated to the P-band radar return signal. The hypothesis of the AirMOSS project is that integrating spatially and temporally resolved observations of RZSM into ecosystem dynamics models can significantly reduce the uncertainty of NEE estimates and carbon balance estimates. The calibration of the sensor systems is described and results are demonstrated for multiday up to annual time series at various sites. The success of fitting four models (e.g., power, first-, second-, and third-order polynomials) to the RZSM content with depth is tested by using in situ data collected in Tonzi Ranch, CA, USA, in 2014. Under the testing condition, all models satisfy the mission goal most of the duration. The second- and third-order models perform better during the dry season than the simpler power and first-order models. However, during periods of rapidly changing values of RZSM content, none of the models are able to meet the root-mean-squared error objective.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Investigation into Different Polarimetric Features for Sea Ice
           Classification Using X-Band Synthetic Aperture Radar
    • Pages: 3131 - 3143
      Abstract: Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH–VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to be more useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Arctic Sea Ice Area Export Through the Fram Strait Estimated From
           Satellite-Based Data:1988–2012
    • Pages: 3144 - 3157
      Abstract: By combing ice motion and concentration fields, we obtain the estimates of ice area flux passing the Fram Strait over a 25-year period (1988–2012). Mean monthly area flux exhibits a prominent cycle with the peak in March $rm{(78 times 10}^{3},text{km}^{2}text{/month)}$ and the trough in August $rm{(5 times 10}^{3},text{km}^{2}/text{month)}$ . Mean net annual ice area flux $rm{(644.1 times 10}^{3},text{km}^{2},text{km}^{2}rm{)}$ is primarily/slightly attributable to winter (October–May)/summer (June–September) outflow of $rm{598.7times 10}^{3},text{km}^{2}$ $rm{(or; 93.0%)/ 45.4 times 10}^{3},text{km}^{2} (rm{or} 7%)$ . However, these quantities need to be considered with great prudence since comparisons with preceding studies indicate distinct difference may exist between calculated Fram Strait sea ice outflow based on varied ice motion datasets. In particular, major bias emerged during winter rather than summer. Trends are also affected due to this bias. Despite the clear disparity in magnitude, we found a good agreement with preceding estimates in terms of temporal variability. The linkage between ice area flux via the Fram Strait and various atmospheric circulation indices, such as the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), a west-east dipole anomaly pattern (DA), as well as a seesaw structure between Beaufort and Barents Sea (BBO), was examined. Results show that atmospheric circulation patterns linked to the DA and BBO possess a relatively stronger influence on Fram Strait ice export over the 25-year period. By contrast, the AO- and NAO-r- lated atmospheric circulation pattern exhibit weaker connections with ice outflow through the strait. In addition, correlation analysis further justify the robust connection with a cross-strait surface level pressure gradient index. Depending on months, it is suggested that a manifold of aforementioned atmospheric indices may jointly or alone impose a significant influence on sea ice export through the Fram Strait.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • The Impact of DEM Resolution on Relocating Radar Altimetry Data Over Ice
    • Pages: 3158 - 3163
      Abstract: Beam-limited footprints from conventional satellite radar altimeters have diameters of up to tens of kilometers. Topography within the footprint results in a displacement of the reflecting point from Nadir to the point of closest approach relative to the satellite. Several methods exist for correcting for such mispointing errors. Here, two techniques are applied to observations near Jakobshavn Isbræ, acquired with Envisat's Radar Altimeter (RA-2). The a priori knowledge on the surface topography is obtained from a digital elevation model. The methods relocate the measurement location horizontally to agree with the measured range. One method assumes a constant surface slope within the footprint and uses this and the surface aspect to estimate the displacement parameter; the other locates the optimal relocation point using local topography. The results of the two methods are evaluated against airborne laser-scanner data from the airborne topographic mapper. We find that the accuracy of the relocation depends on both the technique and the spatial resolution of the digital elevation model, and that this dependency varies with surface roughness. Thus, the relocation may be associated with significant errors, which will lower the accuracy of cryospheric studies based on radar altimetry data. We find that the most accurate results are obtained when assessing the full local topography. Furthermore, errors in data over the steep margin are minimized the most when using a spatial resolution of $2$  km; the effect of the resolution over regions with a smoother topography is minor.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Use of Intensity and Coherence of X-Band SAR Data to Map
           Thermokarst Lakes on the Northern Tibetan Plateau
    • Pages: 3164 - 3176
      Abstract: In order to monitor the response of thermokarst lakes on the Tibetan Plateau to rapid climatic changes and human activities, an automated shoreline extraction approach from the high-resolution TerraSAR/TanDEM-X imagery is proposed. First, a preprocessing scheme is applied to calibrate, coregister, and estimate the intensity and coherence of the imagery. Second, the statistical distribution of intensity and coherence are used to refine the data term of a region-based level set model. A distance regularized term and a variable weighting parameter are also added to improve the algorithm efficiency. Finally, a postprocessing is applied to remove false edges related to spurious segments and generate a vector map by geocoding the detected edges. Experiments on the intensity and coherence images demonstrate that: 1) the Fisher distribution can improve the usability of the level set method for extraction of shorelines using the intensity feature, because it can flexibly model the speckle fluctuations of many land classes; 2) the coherence information can significantly improve the efficiency and accuracy of segmentation by suppressing the misclassification caused by the sands around the lakes and the local winds over the lakes; 3) using a variable parameter to weight the data term and a regular term in a level set model can produce more robust and fast segmentation; and 4) using of intensity and coherence in unison can locate the exact position of most of the shorelines and reduce the relative uncertainty within 3 pixels with 90% confidence level.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Forest Canopy Height Estimation Using Tandem-X Coherence Data
    • Pages: 3177 - 3188
      Abstract: In this paper, we report results of a study aimed at assessing the potential for using X-band single-pass radar interferometric coherence for forest canopy height estimation. We use datasets from the Tandem-X satellite pair collected over Canadian forest test sites, where supporting lidar data are available for validation. We first employ dual-copolarized modes to assess the potential of polarimetric interferometry for forest canopy height retrieval. We show that for this forest type, single polarization modes have better properties, including much improved spatial coverage. We develop a new algorithm for single polarization data and validate the canopy height products against lidar. We then extend the canopy height product over a mosaic of multiple swaths to demonstrate the potential for very wide area forest height mapping using radar coherence.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • The Dual-Band PolInSAR Method for Forest Parametrization
    • Pages: 3189 - 3201
      Abstract: There are basically two methods for retrieving the ground and the canopy height from interferometric synthetic aperture radar (InSAR) in forested areas. The first method is based on the difference between InSAR height estimations from dual-band (DB) systems, typically operating at X- and P-bands HH. The second method is based on the modeling of the polarimetric (Pol) InSAR response along the forest vertical structure, typically at L- or P-band. This paper proposes the combination of both methods, so that the ground and total tree height estimations are improved and become available alongside the interferometric forest height with the usage of polarimetric data and the RVoG model adoption in both bands. In the proposed method (DB-PolInSAR), first, the ground phase is retrieved from the RVoG inversion through a straightforward line fit of the P-Band polarimetric data in the complex plane. Fixing the ground height coming from the previous P-band inversion and applying the RVoG model to the X-band interferometric data, we estimate the total tree height. Repeat-pass dual-polarimetric (HH and HV) P-band data and single-pass three-baseline HH X-band data acquired with the airborne OrbiSAR sensor of Bradar over the Amazon region of Urucu are used to demonstrate the proposed method. Comparisons between the dual-band PolInSAR and the dual-band single-polarization cases are performed for five different P-band single-baseline configurations. Better ground estimation over range is obtained with the proposed method. Furthermore, the three-antenna single-pass X-band data enabled a robust RVoG total tree height inversion.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Examining High-Resolution PiSAR-L2 Textures for Estimating Tropical Forest
           Carbon Stocks
    • Pages: 3202 - 3209
      Abstract: This study examines the potential of airborne PiSAR-L2 data for estimating forest carbon stocks in central Sumatra. Polarimetric interferometric synthetic aperture radar L-band-2 (PiSAR-L2) is a second-generation airborne sensor developed by JAXA. We acquired full-polarimetric data at a fine spatial resolution of 2.5 m during the PiSAR-L2 flight campaign in August 2012. A total of 59 field measurement plots for aboveground forest carbon stocks (AFCSs) were established in same year where AFCS ranged between 4.8 and $253.5; text{Mg}; text{C} ;text{ha}^{-1}$ . The plots comprised natural and plantation forests. These plot-level field data were used for calibrating and validating AFCS estimation models with the SAR data. Various possibilities including direct sigma naught backscatters and their ratios and various types of textures obtained from HH, HV, and VV polarizations were examined by applying regression modeling. The main indicators used for the selection of best potential models in the calibration phase were $text{R}^{2}$ , variable inflation factor (VIF), p-value, and root-mean-squared errors (RMSEs). The potential models were validated using the leave-one-out (LOO) method. The results indicated that a simple combination of backscatters and their ratios provides an AFCS estimate with an RMSE of $42.37; text{Mg}; text{C}; text{ha}^{-1}$ and an $text{R}^{2}$ of 0.65. Inclusion of SAR textural parameters improved the AFCS estimates with an RMSE of $30.93;text{Mg}; text{C}; text{ha}^{-1}$ and an $text{R}^{2}$ of 0.80. Thi- indicates that the airborne PiSAR-L2 full-polarimetric data have the potential to estimate forest carbon stocks with an improved accuracy in the tropical region.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • When Big Data are Too Much: Effects of LiDAR Returns and Point Density on
           Estimation of Forest Biomass
    • Pages: 3210 - 3218
      Abstract: Analysis of light detection and ranging (LiDAR) data is becoming a mainstream approach to mapping forest biomass and carbon stocks across heterogeneous landscapes. However, large volumes of multireturn high point-density LiDAR data continue to pose challenges for large-area assessments. We are beginning to learn when and where point density can be reduced (or aggregated), but little is known regarding the degree to which multireturn data—at varying levels of point density—improve estimates of forest biomass. In this study, we examined the combined effects of LiDAR returns and data reduction on field-measured estimates of aboveground forest biomass in deciduous and mixed evergreen forests in an urbanized region of North Carolina, USA. We extracted structural metrics using first returns only, all returns, and rarely used laser pulse first returns from reduced point densities of LiDAR data. We statistically analyzed relationships between the field-measured biomass and LiDAR-derived variables for each return type and point-density combination. Overall, models using first return data performed only slightly better than models that utilized multiple returns. First-return models and multiple-return models at one percent point density resulted in 14% and 11% decrease in the amount of explained variation, respectively, compared to models with 100% point density. In addition, variance of modeled biomass across all point densities and return models was statistically similar to the field-measured biomass. Taken together, these results suggest that LiDAR first returns at reduced point density provide sufficient data for mapping urban forest biomass and may be an effective alternative to multireturn data.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Measurements of Boreal Coniferous Forest Soil and Humus With an Airborne
    • Pages: 3219 - 3228
      Abstract: Boreal coniferous forests are a large and diverse biome, which has not been widely studied in scope of space borne soil moisture (SM) observations. This paper presents a series of airborne measurements that were made to explore microwave emissivity of a boreal coniferous forest in different moisture conditions. Ground observations of SM and vegetation were made in conjunction with the airborne measurements. The L-band microwave emission of the biosphere (L-MEB) model, which is used in the SM and ocean salinity (SMOS) SM algorithm, was used to model the emission of the forest scenes. The litter layer was included in the modeling because of the thick humus layer present in the study area. The measurements indicate that with sufficient parameters the L-MEB model can simulate the forest and soil-humus ensemble emission with reasonable accuracy ( ${text{R}} = 0.75$ , ${text{RMSE}} = 4.4{text{ K}}$ ). While SM retrieval is hampered by the complex behavior of humus layer, a correct parameterization of the layer is likely to improve accuracy of moisture retrieval for mixed pixels in SMOS observations.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Automated Individual Tree Isolation on High-Resolution Imagery: Possible
           Methods for Breaking Isolations Involving Multiple Trees
    • Pages: 3229 - 3248
      Abstract: One of the key problems in automated individual tree crown delineation, whether from multispectral or lidar data, is the grouping of several trees into a single tree crown outline (isol). Using airborne multispectral imagery, we explored four approaches to breaking such isols into multiple crowns: “core,” “tree top,” “template matching,” and “basin” breaks. Core breaks are made using only isol shape and morphological primitives. Tree top and template matching breaks utilize image maxima and pattern, and watershed drainage basins form the basis of basin breaks. The effectiveness of each of the four break types was assessed against the presence and position of the true boundary between multiple tree crowns and with reference to original isol shape. There was correspondence and differences between breaks of different types. A set of rules was developed to choose a single break when there was positional correspondence of several break types. The rules were based on isol shape type and the break types present. Despite being a complex and difficult issue, it was shown that the concept of identifying poor delineations, recognizing them as cases of multiple trees, and remediating the crown delineations is viable and worthy of further development.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Optical Scale Polarimetric Device for Nanotube Forest Measurement: An
           Opportunity to Anticipate Bistatic Polarimetric SAR Images of Tree Trunk
           Forests at P-Band
    • Pages: 3249 - 3258
      Abstract: This paper investigates a new polarimetric device that produces reduced scale measurements for bistatic radar. The optical scale device is proposed to overcome the lack of real bistatic and full polarimetric radar acquisitions for forest of trunks. It is based on the scale invariant rule that is deduced from the Maxwell equations. From this principle, the electromagnetic (EM) response is kept at the reduced scale if the illumination wavelength is scaled and if the permittivity remains the same. This last condition needs the use of a carefully chosen material at optical scale. Nanoscale scenes are composed of carbon nanotubes (CNTs). They present several advantages: their arrangement is similar to the structure of a forest of trunks, their shape, and density can be controlled, it is possible to create very large scenes composed of thousands of elements, and finally their permittivity at optical wavelength is about the same order of magnitude than the permittivity of the tree trunks at radar wavelength. They are measured under the view of a microscope objective with a 633-nm wavelength laser playing the role of the EM source. The device images at once the forest polarimetric response, for a given transmitter location, and for the entire set of angular positions of reception. Two samples of forests with different densities are measured. They are analyzed using the Cloude and Pottier decomposition and the Lu and Chipman decomposition. The produced results are unprecedented on forest-like environment and it would be very helpful for the radar community.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Estimating Leaf Area Index of Maize Using Airborne Discrete-Return LiDAR
    • Pages: 3259 - 3266
      Abstract: The leaf area index (LAI) is an important vegetation biophysical parameter, which plays a critical role in gas-vegetation exchange processes. Several studies have recently been conducted to estimate vegetation LAI using airborne discrete-return Light Detection and Ranging (LiDAR) data. However, few studies have been carried out to estimate the LAI of low-statue vegetation, such as the maize. The objective of this research is to explore the potential of estimating LAI for maize using airborne discrete-return LiDAR data. The LAIs of maize were estimated by a method based on the Beer–Lambert law and a method based on the allometric relationship, respectively. In addition, a new height threshold method for separating ground returns from canopy returns was proposed to better estimate the LAI of maize. Moreover, the two LAI estimation methods were also evaluated using the leave-one-out cross-validation method. Results indicate that the new height threshold method performs better than the traditional height threshold method in separating grounds returns from LiDAR returns. The coefficient of variation of detrended return heights within a field was a good parameter to estimate the LAI of maize. In addition, results also indicate that the method based on the Beer–Lambert law (R 2 = 0.849, RMSE = 0.256) was more accurate than the method based on the allometric relationship (R 2 = 0.779, RMSE = 0.315) in low-LAI regions, while only the method based on the allometric relationship is suitable for estimating the LAI of maize in high-LAI regions.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Evaluating Spatial Representativeness of Station Observations for Remotely
           Sensed Leaf Area Index Products
    • Pages: 3267 - 3282
      Abstract: Continuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-to-area consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • A Novel Nonlinear Algorithm for Area-Wide Near Surface Air Temperature
    • Pages: 3283 - 3296
      Abstract: This paper reports a novel nonlinear algorithm for retrieving near surface air temperature over a large area using support vector machines with satellite remote sensing and other types of data. The steps include the following. 1) Establish the 1 $^{text{st}}$ sub model learning dataset and validation dataset, then obtain the 2 $^{text{nd}}$ to f $^{text{th}}$ sub model learning datasets and validation datasets, using unmanned weather station data and predefined influential variables. 2) Retrieve Ta of the target area. 3) Correct the generated Ta images based on prediction errors using the inverse distance weighting interpolation. The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover, DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a ${text{CPU}} + {text{GPU}}$ isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-mean-square errors, and the percentage of data points with  < 3 °C absolute error. Our SVM approach overcomes shortcomings of classical temperature remote sensin- technologies, and is the first report of such application.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Comparison of Atmospheric Methane Retrievals From AIRS and IASI
    • Pages: 3297 - 3303
      Abstract: Atmospheric methane (CH4) is a standard product of the atmospheric infrared sounder (AIRS) aboard NASA's Aqua satellite, generated at the NASA Goddard Earth Sciences Data and Information Services Center (NASA/GES/DISC), and a product of the infrared atmospheric sounding interferometer (IASI) aboard METOP-A,-B, generated at National Oceanic and Atmospheric Administration's Comprehensive Large Array-data Stewardship System. In order to understand the capability of these two sensors in observing the spatial and temporal distribution of CH4 , this paper compares the CH4 products from AIRS and IASI with aircraft measurements, as well as the corresponding time series in tropics and high northern latitude regions. It is found that the mean degree of freedom from AIRS is smaller than IASI by –0.049 ± 0.152, and in their peak sensitive altitude between 350 and 650 hPa their difference (AIRS – IASI) is about 2.8 ± 17.2 ppb. Both AIRS and IASI can capture the latitudinal gradient, but there is a large scattering in the high northern latitude regions. They agree well in observing the summer enhancement of CH4 during the Monsoon season over South Asia, and the seasonal cycles over Siberia (except for a relatively larger difference in the cold season). These results highlight that AIRS and IASI can provide valuable information to capture the spatiotemporal variation of CH4 in the mid-upper troposphere in most periods and regions, but it is needed to further improve the data quality to make a consistent product using both sensors.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Hierarchical Filtering Strategy for Registration of Remote Sensing Images
           of Coral Reefs
    • Pages: 3304 - 3313
      Abstract: Registration of remote sensing images of coral reefs is basis for detection and analysis of changes to coral reefs, which is difficult due to inadequate and unstable texture information. Affine invariant features matching (AIFM) method, combining maximally stable extremal region (MSER) detector and scale invariant feature transformation (SIFT) descriptor, followed by optimizing using RANdom SAmple Consensus (RANSAC), still cannot lead to satisfactory results of image registration. To address this problem, we propose a hierarchical filtering strategy for image registration, which is composed of two stages. Filter 1, constructed based on a geometric transformation model determined by the corresponding contours of coral reefs, is employed to remove the wrong matching pairs which obviously dissatisfy spatial distributions of matching features. Based on the filtering results of Filters 1 and 2, with an appropriate threshold value determined by overlapping ratio of the corresponding coral reefs, can lead to further optimized results for image registration. This threshold is used to filter all erroneous matching pairs, while retaining as many correct matching pairs as possible. To test the effect of these filters, we design two experiments to compare the four approaches: AIFM without filtering, AIFM with Filter 1, AIFM with Filter 2, and AIFM with hierarchical filtering ( ${bf{Filter}};{bf{1}} + {bf{Filter}};{bf{2}}$ ). The experimental results demonstrate that the approach with hierarchical filtering performs much better than the other approaches.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Data Assimilation of PROBA-V 100 and 300 m
    • Pages: 3314 - 3325
      Abstract: The project for on-board autonomy-vegetation (PROBA-V) satellite can produce global daily images at 300-m spatial resolution. Three sensors are mounted on the same platform. Two off-nadir-viewing sensors acquire imagery at 300-m spatial resolution, whereas a nadir-viewing sensor acquires imagery at 100-m spatial resolution. The swath of the nadir-viewing sensor is only half of the swath of a single off-nadir-viewing sensor. Using this sensor only, the revisit time is five days. Here, we present a data assimilation method to increase the temporal resolution of the 100-m product. The method implements a Kalman filter recursive algorithm that integrates the images at 100 and 300-m resolution to generate the assimilated imagery at the fine spatial detail (100 m). The proposed method can be applied for global products. In this study, it has been applied to a region in western Europe (Flanders) during the growing season. This region is particularly challenging due to frequent cloud cover (45% cloud cover on average). The assimilated product is a cloud-free time series at the temporal resolution of the 300-m data, while preserving the spatial detail of the 100-m data. Quantitative results show the potential of the method compared to a simple data assimilation and the Savitzky–Golay (SG) filter. The added value of the improved spatial resolution from 300 to 100 m has also been illustrated for monitoring agriculture via remote sensing in this area.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Uncertainty in Soil Moisture Retrievals Using the SMAP Combined
           Active–Passive Algorithm for Growing Sweet Corn
    • Pages: 3326 - 3339
      Abstract: The baseline active and passive (AP) algorithm of the NASA Soil Moisture Active Passive (SMAP) mission disaggregates the brightness temperature ( ${T}_text{B}$ ) from a spatial resolution of 36 km to 9 km for the soil moisture (SM) using the radar backscattering coefficient ( $sigma ^0$ ) at 3 km. This algorithm was derived based upon an assumption of a linear relationship between ${T}_text{B}$ and $sigma ^0$ . In this study, we investigated the robustness of this assumption with plot-scale AP measurements obtained under different conditions of surface roughness and stages of growing sweet corn. The uncertainties in the estimated ${T}_text{B}$ at 9 km and, hence, the retrieved SM, due to uncertainties in the algorithm parameters, $beta$ and $Gamma$ , were assessed under different landcover heterogeneities. Overall, the linear regression was robust, with $r^2$ $>$ 0.75 under bare soil conditions when surface scattering is dominant and $>$ 0.52 during the growing season. The uncertainties in $beta$ and $Gamma$ due to AP observations result in uncertainties in retrieved SM $
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover
    • Authors: Mitraka; Z.;Del Frate, F.;Carbone, F.;
      Pages: 3340 - 3350
      Abstract: The high spatial diversity of man-made structures, the spectral variability of urban materials, and the three-dimensional structure of the cities make the mapping of urban surfaces using Earth Observation data, one of the most challenging tasks in remote sensing field. Spectral unmixing techniques can be proven useful with medium spectral resolution data to assess urban surface cover information on a subpixel level. Due to the large spectral variability of urban materials and the multiple scattering of light between surfaces in urban areas, multiple endmembers should be used, and the nonlinearity of spectral mixture should be accounted for. In this study, these issues are addressed using an artificial neural network trained with endmember and nonlinearly mixed synthetic spectra to inverse the pixel spectral mixture in Landsat imagery. A spectral library is built, consisting of endmember spectra collected from the image and synthetic spectra, produced using a nonlinear model specifically developed for urban areas. The method was tested over a case study, and the validation against higher resolution products revealed an accuracy of around 90% for all abundance maps. The comparison performed between the linear and nonlinear implementation of the method proved the need for including the nonlinear term, especially for improving the built-up abundance map. The proposed method is easily transferable to any city and fast in terms of computations, which makes it ideal for the implementation of operational urban services.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Introducing IEEE collabratec
    • Pages: 3351 - 3351
      Abstract: 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
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
  • Expand Your Network, Get Rewarded
    • Pages: 3352 - 3352
      Abstract: Advertisement, IEEE.
      PubDate: July 2016
      Issue No: Vol. 9, No. 7 (2016)
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