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  Subjects -> ELECTRONICS (Total: 154 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: 17)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 148)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 19)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Archives of Electrical Engineering     Open Access   (Followers: 11)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 24)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 26)
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: 35)
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: 82)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 68)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 50)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 52)
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)
Energy Storage Materials     Full-text available via subscription  
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 12)
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: 61)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 44)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 36)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 25)
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: 40)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 35)
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: 26)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 11)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 17)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 46)
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: 12)
IET Power Electronics     Hybrid Journal   (Followers: 23)
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: 8)
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: 9)
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 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: 4)
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 Energy Storage     Full-text available via subscription  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 104)
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: 9)
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: 18)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 13)
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: 4)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 12)
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: 42)
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: 52)
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)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 10)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.196]   [H-I: 37]   [42 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: The IEEE GRSS Society is grateful for the support given by the organizations listed and invites applications for Institutional Listings from other firms.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Information for Authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Provides a listing of the editors, board members, and current staff for this issue of the publication.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Frontcover
    • Abstract: Presents the ront cover for this issue of the publication.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Foreword to the Special Issue on Analysis of Multitemporal Data and
           Applications
    • Pages: 3356 - 3358
      Abstract: The papers in this special issue were presented at the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp 2015) and 2nd EARSeL International Workshop on Temporal Analysis of Satellite Images (Temporal Analysis 2015).
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • A Relative Density Ratio-Based Framework for Detection of Land Cover
           Changes in MODIS NDVI Time Series
    • Pages: 3359 - 3371
      Abstract: To improve statistical approaches for near real-time land cover change detection in nonGaussian time-series data, we propose a supervised land cover change detection framework in which a MODIS NDVI time series is modeled as a triply modulated cosine function using the extended Kalman filter and the trend parameter of the triply modulated cosine function is used to derive repeated sequential probability ratio test (RSPRT) statistics. The statistics are based on relative density ratios estimated directly from the training set by a relative unconstrained least squares importance Fitting (RULSIF) algorithm, unlike traditional likelihood ratio-based test statistics. We test the framework on simulated, synthetic, and real-world beetle infestation datasets, and show that using estimated relative density ratios, instead of assuming the individual density functions to be Gaussian or approximating them with Gaussian Kernels, in the RSPRT statistics achieves better performance in terms of accuracy and detection delay. We verify the efficiency of the proposed approach by comparing its performance with three existing methods on all the three datasets under consideration in this study. We also propose a simple heuristic technique that tunes the threshold efficiently in difficult cases of near real-time change detection, when we need to take three performance indices, namely, false positives, false negatives, and mean detection delay, into account simultaneously.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • A Simple Transformation for Visualizing Non-seasonal Landscape Change From
           Dense Time Series of Satellite Data
    • Authors: Jennifer N. Hird;Guillermo Castilla;Greg J. McDermid;Inacio T. Bueno;
      Pages: 3372 - 3383
      Abstract: We present the Change, Aftereffect, and Trend (CAT) transform for visualizing and analyzing landscape dynamics from dense, multi-annual satellite vegetation index (VI) time series. The transform compresses a temporally detailed, multi-annual VI dataset into three new variables capturing change events and trends occurring within that period. First, peak annual greenness is extracted from each year. Then a series of simple calculations generate the three CAT variables: 1) Change: the maximum interannual absolute difference in peak greenness between consecutive years; 2) Aftereffect: the mean peak greenness after Change occurred; and 3) Trend: the slope of a linear regression applied to the entire annual peak greenness time series. We demonstrate the CAT transform by applying it to a MODIS 16-day 250-m normalized difference VI (NDVI) dataset covering the province of Alberta, Canada, for 2001 through 2011. We find that the CAT variables capture much of the non-seasonal change in the original NDVI time series. When displayed as an RGB color composite (the CAT image), the transform provides a striking visualization of both drastic and gradual decadal-scale landscape dynamics. Its application to quantitative analyses is demonstrated by an urban sprawl case study conducted around the city of Calgary, Alberta, where a simple decision-tree-based classification of the CAT transform variables was superior to a bitemporal, image-differencing approach. The simple yet powerful CAT transform is easily applicable to other study areas and datasets, and could foster a wider usage and understanding of the many archived high-temporal-resolution satellite datasets currently available.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Learning Relationship for Very High Resolution Image Change Detection
    • Authors: Chunlei Huo;Keming Chen;Kun Ding;Zhixin Zhou;Chunhong Pan;
      Pages: 3384 - 3394
      Abstract: The difficulty of very high resolution image change detection lies in the low interclass separability between the changed class and the unchanged class. According to experiments, we found that this separability can be improved by mining the relationship contained in the training samples. Based on this observation, a supervised change detection approach is proposed in this paper based on relationship learning. The proposed approach begins with enriching the training samples based on their neighborhood relationship and label coherence; this relationship is then learned simultaneously with the classifier, and, finally, the latter classification performance benefits from the learned relationship. Experiments demonstrate the effectiveness of the proposed approach.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Discriminative Random Fields Based on Maximum Entropy Principle for
           Semisupervised SAR Image Change Detection
    • Authors: Lin An;Ming Li;Peng Zhang;Yan Wu;Lu Jia;Wanying Song;
      Pages: 3395 - 3404
      Abstract: This paper proposes a novel semisupervised SAR images change detection algorithm using discriminative random fields based on maximum entropy principle (MEDRF). MEDRF is a discriminative model fused by two generative models, named as the bias model and the correction model, based on maximum entropy (ME) principle. In MEDRF model, we construct the bias model and the correction model on labeled samples and unlabeled samples, respectively, based on Markov random fields (MRF) to capture the multitemporal image information. Then, we deduce two constraints from the two generative models, and thus fuse the bias model and the correction model to derive MEDRF model according to ME principle subjected to the two constraints. In this way, the proposed MEDRF takes full advantages of the image information from the labeled samples and the unlabeled samples, especially including the spatial-contextual information, to provide an appropriate class boundary. In the experiment, we analyze the influence of the number of labeled samples to the performance of MEDRF model in semisupervised change detection to illustrate that MEDRF can achieve appropriate detection results even using a small number of labeled samples, and the experimental results on real SAR data demonstrate MEDRF model is able to achieve improvement in change detection over several methods proposed recently.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Remote-Sensing Image Change Detection With Fusion of Multiple Wavelet
           Kernels
    • Authors: Lu Jia;Ming Li;Peng Zhang;Yan Wu;Lin An;Wanying Song;
      Pages: 3405 - 3418
      Abstract: Quality of the difference image determines the potentials of the change detection algorithms. The subtraction operation and ratio operation are two commonly used tools for producing the difference image. However, the complementary information existing in the two difference images, the subtraction image and the ratio image, has found limited applications in real tasks by now. Therefore, a method which utilizes multiple wavelet kernels for fusing the complementary information of the two difference images is proposed in this paper for remote-sensing image change detection. First, the complementary information of the two difference images is analyzed. That is, the subtraction operation highlights the changed areas and the ratio operation suppresses the disturbance of the complex background. Then, for each difference image, wavelet kernels at multiple scales are computed followed by a reliable scale selection scheme based on correlation coefficients. After that, the two difference images' wavelet kernels at reliable scales are fused under the supervision of an initial change detection result. The obtained kernel, the MWF kernel, is of good homogeneity and smoothness on the changed areas as well as great suppression of the complex background's disturbance, since it takes into account the complementary information of the two difference images. The principal component analysis (PCA) and k-means clustering act on the subtraction image to produce the initial change detection result. Finally, the fused kernel is inputted into a classification algorithm based on the minimum Euclidean distance in the kernel space to get the final change detection result. Experiments demonstrate the effectiveness of the proposed method and illustrate that it possesses both strong disturbance immunity and good homogeneity of changed areas for remote-sensing image change detection.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Multitemporal SAR Image Decomposition into Strong Scatterers, Background,
           and Speckle
    • Pages: 3419 - 3429
      Abstract: Speckle phenomenon in synthetic aperture radar (SAR) images makes their visual and automatic interpretation a difficult task. To reduce strong fluctuations due to speckle, total variation (TV) regularization has been proposed by several authors to smooth out noise without blurring edges. A specificity of SAR images is the presence of strong scatterers having a radiometry several orders of magnitude larger than their surrounding region. These scatterers, especially present in urban areas, limit the effectiveness of TV regularization as they break the assumption of an image made of regions of constant radiometry. To overcome this limitation, we propose in this paper an image decomposition approach. There exist numerous methods to decompose an image into several components, notably to separate textural and geometrical information. These decomposition models are generally recast as energy minimization problems involving a different penalty term for each of the components. In this framework, we propose an energy suitable for the decomposition of SAR images into speckle, a smooth background, and strong scatterers, and discuss its minimization using max-flow/min-cut algorithms. We make the connection between the minimization problem considered, involving the L0 pseudonorm, and the generalized likelihood ratio test used in detection theory. The proposed decomposition jointly performs the detection of strong scatterers and the estimation of the background radiometry. Given the increasing availability of time series of SAR images, we consider the decomposition of a whole time series. New change detection methods can be based on the temporal analysis of the components obtained from our decomposition.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Building Change Detection Using High Resolution Remotely Sensed Data and
           GIS
    • Authors: Natalia Sofina;Manfred Ehlers;
      Pages: 3430 - 3438
      Abstract: Remote sensing technology is increasingly being used for rapid detection and visualization of changes caused by catastrophic events. This paper presents a semi-automated feature-based approach to the identification of building conditions especially in affected areas using geographic information systems (GIS) and remote sensing information. For image analysis, a new “detected part of contour” (DPC) feature is developed for the assessment of building integrity. The DPC calculates a part of the building contour that can be detected in the remotely sensed image. Additional texture features provide information about the area inside the buildings. The effectiveness of the proposed method is proved by high overall classification accuracy for two different study cases. The results demonstrate that the “map-to-image” strategy enables extracting valuable information from the remotely sensed image for each individual vector object, thereby being a better choice for change detection within urban areas.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Automatic Change Detection in High-Resolution Remote Sensing Images by
           Using a Multiple Classifier System and Spectral–Spatial Features
    • Authors: Kun Tan;Xiao Jin;Antonio Plaza;Xuesong Wang;Liang Xiao;Peijun Du;
      Pages: 3439 - 3451
      Abstract: Change detection (CD) is an active research topic in remote sensing applications including urban studies, disaster assessment, and deforestation monitoring. In this paper, we propose an automatic method for CD in high-resolution remote sensing images that uses a novel strategy for the selection of training samples and an ensemble of multiple classifiers. As for the selection of training samples, our proposed method uses two groups of thresholds instead of just one threshold to enhance the quality of the selected training samples by allowing for their selection in an intelligent manner. In order to achieve high CD accuracy, spatial information such as texture and morphological profiles are utilized in conjunction with spectral information. Our multiple classifier system (MCS) exploits the extreme learning machine (ELM), multinomial logistic regression (MLR), and K-nearest neighbor (KNN) classifiers. To validate our newly proposed approach, we conduct experiments using multispectral images collected by ZY-3. The proposed method provides state-of-the-art CD accuracies as compared with other approaches widely used in the literature for CD purposes.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • SAR Images Change Detection Based on Spatial Coding and Nonlocal
           Similarity Pooling
    • Authors: Shaona Wang;Licheng Jiao;Shuyuan Yang;
      Pages: 3452 - 3466
      Abstract: Accurate detection of the changed areas and effective speckle suppression are the main difficulties in synthetic aperture radar (SAR) image change detection (CD). The available feature extraction techniques for CD always ignore the spatial context correlation and are not robust to speckle noise. To overcome these drawbacks, we present a novel feature extraction technique that takes full advantage of sparse representation (SR) and nonlocal similarity of SAR images. First, each pixel in the difference image is represented by a feature vector, which is extracted using the sparse coding with a constructed robust discriminative dictionary. Next, a group of related feature vectors for each pixel can be generated according to the nonlocal similarity of SAR image. Finally, the discriminative change feature is obtained by means of the pooling, which can extract significant change information from the feature group. This method not only suppresses the speckle noise effectively but also improves the discrimination of the extracted features. The experimental results verify the superior performance of the proposed method on several real SAR image data sets and simulated image pairs.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Individual Tree Crown Modeling and Change Detection From Airborne Lidar
           Data
    • Authors: Wen Xiao;Sudan Xu;Sander Oude Elberink;George Vosselman;
      Pages: 3467 - 3477
      Abstract: Light detection and ranging (lidar) provides a promising way of detecting changes of trees in three-dimensional (3-D) because laser beams can penetrate through the foliage and therefore provide full coverage of trees. The aim is to detect changes in trees in urban areas using multitemporal airborne lidar point clouds. Three datasets covering a part of Rotterdam, The Netherlands, have been classified into several classes including trees. A connected components algorithm is applied first to cluster the tree points. However, closely located and intersected trees are clustered together as multi-tree components. A tree-shaped model-based continuously adaptive mean shift (CamShift) algorithm is implemented to further segment these components into individual trees. Then, the tree parameters are derived in two independent methods: a point-based method using the convex hull and a model-based method which fits a tree-shaped model to the lidar points. At last, changes are detected by comparing the parameters of corresponding tree models which are matched by a tree-to-tree matching algorithm using overlapping bounding boxes and point-to-point distances. The results are visualized and statistically analyzed. The CamShift using a tree model kernel yields high segmentation accuracies. The model-based change detection is consistent with the point-based method according to the small differences between the parameters of single trees. The highlight is that it is more robust to data noise and to the segmentation of multi-tree components compared to the point-based method. The detected changes show the potential of the method to monitor the growth of urban trees.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Change Detection Based on Conditional Random Field With Region Connection
           Constraints in High-Resolution Remote Sensing Images
    • Authors: Licun Zhou;Guo Cao;Yupeng Li;Yanfeng Shang;
      Pages: 3478 - 3488
      Abstract: In this paper, a novel change detection method based on conditional random field (CRF) with region connection constraints in multitemporal high-resolution remote sensing images is proposed. The change detection problem is formulated as a labeling issue to discriminate the changed class from the unchanged class in the difference image. In the CRF model, the unary potential is described by using the memberships of unsupervised fuzzy C-means clustering algorithm. The pairwise potential adopts a boundary constraint based on Euclidean distance. In addition, region iteration potential defined on a set of pixels is incorporated into CRF model to suppress the oversmooth performance. A chief advantage of our approach is to be able to achieve correct change map and avoid training a large number of model parameters. Experimental results demonstrate that the proposed method improves the change detection accuracy, is more robust against noise than other state-of-the-art approaches, and preserves boundary information.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Extending Airborne Lidar-Derived Estimates of Forest Canopy Cover and
           Height Over Large Areas Using kNN With Landsat Time Series Data
    • Authors: Oumer S. Ahmed;Steven E. Franklin;Michael A. Wulder;Joanne C. White;
      Pages: 3489 - 3496
      Abstract: Airborne light detection and ranging (lidar) data provide an accurate and consistent means to obtain reliable forest canopy cover (CC) and height measurements, which are important in determining forest stand structure, volume, and biomass. Extending CC and height measurements over larger areas by integration with satellite imagery increases the value of airborne lidar data. A typical approach has been to use multiple regression, machine-learning, or regression tree methods to determine relationships between the forest structure variables measured in the lidar data and available single-date or multitemporal Landsat sensor reflectance data, and if the relationships are strong enough, to extend those variables over much larger areas than is typically covered by lidar. Such methods can be difficult to apply because of the complexity of the extending models and algorithms, and the long processing times, which may become prohibitive. One machine-learning approach, which uses the k-nearest neighbor (kNN) algorithm, is presented here in a British Columbia, Canada forest environment. Our goal was to simplify the estimation of lidar-derived forest structure variables with available Landsat time series data and compare the results of the kNN model to the traditional multiple regression results, and to those obtained with more complex and computationally demanding random forest (RF) methods. We develop and test the kNN model with airborne lidar-derived estimates of forest CC and height in 1846 relatively young and mature forests. The best kNN model produced estimates of airborne lidar-derived CC in validation sample (n = 1132) of mature forest with three Landsat time series variables with an R2 = 0.74 and RMSE of approximately 10%. This result is comparable to the results obtained in earlier work using more complex machine-learning approaches (in approximately 1/10 the time). Younger (i.e., recently disturbed) forest CC estimation was less successful in the kNN mode- because of the high degree of structural variability in these forests. Extending airborne lidar estimates of forest height to larger areas was also possible though, as expected, less successful than CC estimation using the kNN approach.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Analysis of Polarimetric Radar Data and Soil Moisture From Aquarius:
           Towards a Regression-Based Soil Moisture Estimation Algorithm
    • Authors: Mariko S. Burgin;Jakob J. van Zyl;
      Pages: 3497 - 3504
      Abstract: Many soil moisture radar retrieval algorithms depend on substantial amounts of ancillary data, such as land cover type and soil composition. To address this issue, we examine and expand an empirical approach by Kim and van Zyl as an alternative; it describes radar backscatter of a vegetated scene as a linear function of volumetric soil moisture, thus reducing the dependence on ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived soil moisture data to determine the two polarization dependent parameters on a global scale and on a weekly basis. We propose a look-up table based soil moisture estimation approach; it is promising due to its simplicity and independence of ancillary data. However, the estimation performance is found to be impacted by the used land cover classification scheme. Our results show that the sensitivity of the radar signal to soil moisture changes seasonally, and that the variation differs depending on vegetation class. While this seasonal variation can be relatively small, it must be properly accounted for as it impacts the soil moisture retrieval accuracy.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Equatorial Forests Display Distinct Trends in Phenological Variation: A
           Time-Series Analysis of Vegetation Index Data from Three Continents
    • Pages: 3505 - 3511
      Abstract: Recent studies have questioned the applicability of satellite-derived vegetation indices (VIs) for evaluating phenological variation in tropical forests, due to potential artifacts caused by the bidirectional reflectance distribution function (BRDF). For nadir-normalized data, BRDF will be driven principally by intraannual variation in solar elevation. Where areas lying on the same latitude are under similar solar elevation “regimes,” if the observed variation in VIs is indeed driven by BRDF, then different regions at the same latitude should display identical VI variations. That hypothesis was tested by comparing VI data for tropical evergreen forests in three zones north of the equator (the Guianas, central Africa, and northern Borneo). Enhanced vegetation index, the fraction of green vegetation cover, and leaf area index (LAI) from MODIS and SPOT VEGETATION ultimately showed that VI trends for the regions differ greatly. The trend for Borneo's forests is generally flat over the 12 years studied, while data for the Guianas and central Africa both exhibit strong but distinct seasonal patterns. Correlation analyses indicate that the VI trends between zones are neither strongly correlated to each other nor to variation in solar elevation (except in central Africa), suggesting that the observed variation in the VIs is not driven by BRDF. In contrast, regression analysis indicated that for the Guianas and central Africa, VI variation was most explained by variation in environmental factors, but not atmospheric effects, suggesting seasonally driven phenology.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Contribution to Real-Time Estimation of Crop Phenological States in a
           Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and
           Temperature
    • Authors: Caleb De Bernardis;Fernando Vicente-Guijalba;Tomas Martinez-Marin;Juan M. Lopez-Sanchez;
      Pages: 3512 - 3523
      Abstract: In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Satellite Gravimetric Estimation of Groundwater Storage Variations Over
           Indus Basin in Pakistan
    • Authors: Naveed Iqbal;Faisal Hossain;Hyongki Lee;Gulraiz Akhter;
      Pages: 3524 - 3534
      Abstract: Like other agrarian countries, Pakistan is now heavily dependent on its groundwater resources to meet the irrigated agricultural water demand. Groundwater has emerged as a major source with more than 60% contribution in total water supplies. In the absence of groundwater regulation, the uneven and overexploitation of groundwater resource in Indus Basin has caused several problems of water table decline, groundwater mining, and deterioration of groundwater quality. This study evaluates the potential of Gravity Recovery and Climate Experiment Satellite (GRACE)-based estimation of changes in groundwater storage (GWS) as a cost-effective approach for groundwater monitoring and policy recommendations for sustainable water management in the Indus basin. The GRACE monthly gravity anomalies from 2003 to 2010 were analyzed as total water storage (TWS) variations. The variable infiltration capacity hydrological model-generated soil moisture and surface runoff were used for the separation of TWS into GWS anomalies. The GRACE-based GWS anomalies are found to favorably agree with trends inferred from in situ piezometric data. A general depletion trend is observed in Upper Indus Plain (UIP) where groundwater is found to be declining at a mean rate of about 13.5 mm per year in equivalent height of water during 2003–2010. A total loss of about 11.82 km3 per year fresh groundwater stock is inferred for UIP. Based on TWS variations and ground knowledge, the two southern river plains, Bari and Rechna are found to be under threat of extensive groundwater depletion. GRACE TWS data were also able to pick up signals from the large-scale flooding events observed in 2010 and 2014. These flooding events played a significant role in the replenishment of the groundwater system in Indus Basin. Our study indicates that the GRACE-based estimation of GWS changes is skillful enough to provide mo-thly updates on the trend of the GWS changes for resource managers and policy makers of Indus basin.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Assessment of the Evolution of Nitrate Deposition Using Remote Sensing
           Data Over the Yangtze River Delta, China
    • Authors: Miaomiao Cheng;Zheng Guo;Hongyan Dang;Youjiang He;Guorui Zhi;Jian Chen;Yujie Zhang;Weiqi Zhang;Fan Meng;
      Pages: 3535 - 3545
      Abstract: Along with the increasing concentration of nitrogenous pollutants emitted from the combustion and fertilizers, atmospheric nitrogen (N) deposition has become a great concern due to its significant ecological effect, especially in severe N emission regions such as the Yangtze River Delta (YRD) in east China. The spatial and temporal nitrate deposition fluxes were conducted using satellite data in YRD from 1996 to 2011. Our study reveals significant spatial variations of nitrate deposition in YRD region. In general, the fluxes of total (dry plus wet) nitrate deposition in YRD were up to 22.03 kg·N·ha-1·yr-1 with large loading received in winter. Most high fluxes appeared over urban (37.72 kg·N·ha-1·yr-1) and cropland (30.29 kg·N·ha-1·yr-1) areas. During the study period (1996-2011), a significant increasing trend of nitrate deposition was clearly observed in YRD with an annual rate of 1.33 kg·N·ha-1·yr-1. The spatial patterns of estimated nitrate deposition also showed that there were much higher fluxes and annual increasing trend in the middle region of YRD, i.e., the metropolitan areas contained Shanghai-Nanjing-Hangzhou cities, than in other areas. Our results also reveal that dry nitrate deposition contributed more than 50% of the total nitrate deposition over all provinces and land covers except coastal sea (14.27%), which indicates the relative importance of dry deposition to the total nitrate deposition in the YRD region. Therefore, it is necessary to consider both dry and wet deposition when evaluating the influences of nitrate deposition on environment and ecosystem health.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Prediction of Land-Surface Temperatures of Jaipur City Using Linear Time
           Series Model
    • Authors: Aneesh Mathew;Sreenu Sreekumar;Sumit Khandelwal;Nivedita Kaul;Rajesh Kumar;
      Pages: 3546 - 3552
      Abstract: All cities of the world have undergone rapid urbanization. Consequently, urban areas encounter higher surface and air temperatures than the surrounding nonurbanized areas and exhibit urban heat island (UHI) effect. Surface temperature derived from remote sensing data has been used for analyzing the UHI effect over a number of cities. This study has been carried out to predict the land-surface temperature (LST) of Jaipur city, India. Remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors have been used for the prediction. 10-year linear time series (LTS) model has been developed using enhanced vegetation index (EVI), elevation, and LST for the prediction of future LST. Model output has been validated using LST data of the year 2014. A comparison of model-estimated LST and measured LST shows that mean absolute error (MAE) varies from 0.292 to 0.353 and mean absolute percentage error (MAPE) varies from 0.098 to 0.123. High correlation exists between the model-estimated LST and measured LST with an average {R}^{2} value of 0.95. LTS model developed in this study can be used for many studies involving LST, and it can be a significant tool for the prediction of UHI effect at any location.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Data Uncertainty in an Improved Bayesian Network and Evaluations of the
           Credibility of the Retrieved Multitemporal High-Spatial-Resolution Leaf
           Area Index (LAI)
    • Authors: Wenchao Han;Yonghua Qu;
      Pages: 3553 - 3563
      Abstract: Integration of multisource remote sensing data is one of the methods to invert temporal high-spatial-resolution (time-continuous and with the resolution in 10-m scale) leaf area index (LAI). However, a few studies are related to addressing the uncertainty of data sources in the inversion algorithm and investigating the relationship between the uncertainty of data sources and the credibility of inversion results. This research is designed to retrieve temporal high-resolution LAI using an improved dynamic Bayesian network approach to fuse the dynamic change information of coarse-resolution historical data with the spatial information of high-resolution remote sensing observations. In this process, the focus was on handling the uncertainty of data sources that is mainly derived from the uncertainty of high-resolution remote sensing observations. On the basis of retrieving the temporal high-resolution LAI, the credibility of the inversion results was calculated and the influence of data source uncertainty on inversion results was investigated. To implement the work framework, this study takes the Xiaoman irrigation area in the arid middle reaches of the Heihe region as the study area, the uncertainty generated during the Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) atmospheric correction process as the uncertainty in the data sources and the ASTER images as the remote sensing information, and uses the Moderate-resolution Imaging Spectroradiometer MCD15A2 historical LAI data to construct the dynamic LAI information. By constructing an improved dynamic Bayesian network, the LAI products with 15-m spatial resolution and 8-day time-series resolution were produced. The validation results revealed that the determination coefficient R2 between LAI inversion results and actual measured values is 0.85, and the root-mean-square error (RMSE0) is 0.40 m2/m2. It was also observed that the high-resolution observation inform-tion can be severed to gradually correct the dynamic growth information during the time series inversion. This finding is manifested by the fact that with the addition of high-resolution remote sensing observation data, the reliability of the inversion results gradually increases. Meanwhile, the uncertainty of the data sources has a relatively impact on the reliability of the inversion results. When the uncertainty level of data sources is lower than 0.24, the reliability of the inversion results is high. It is concluded that the reliability of LAI will increase with the decreasing of the uncertainty level of remotely sensed data source .
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • A Functional Form for the Diurnal Variation of Lake Surface Temperature on
           Lake Hartwell, Northwestern South Carolina
    • Authors: J. L. Hodges;J. R. Saylor;N. B. Kaye;
      Pages: 3564 - 3577
      Abstract: Satellite measurements of water surface temperature can benefit several environmental applications such as predictions of lake evaporation, meteorological forecasts, and predictions of lake overturning events, among others. Limitations on the temporal resolution of satellite measurements can restrict these improvements. A model of the diurnal variation in lake surface temperature could potentially increase the effective temporal resolution of satellite measurements of surface temperature, thereby enhancing the utility of these measurements in the above applications. As a step in this direction, herein a one-dimensional thermal model of a lake is used in combination with surface temperature measurements from the moderate resolution imaging spectroradiometer instrument aboard the Aqua and Terra satellites, along with ambient atmospheric conditions from local weather stations, to calculate the diurnal surface temperature variation for Lake Hartwell in South Carolina. The calculated solutions are used to obtain a functional form for the diurnal surface temperature variation of this lake, a result which has not been obtained heretofore. This functional form was obtained by averaging over several years worth of data and, therefore, represents the diurnal variation of surface temperature of the average day. Accordingly, attempts to use this averaged function to predict surface temperature in between satellite overpasses on any given day did not perform well due to day-to-day variations in cloud cover, wind speed, and other factors. It is possible that use of this averaged function combined with daily meteorological data may enable better performance.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Using Geoindicators to Prioritize Regional Wetland Locations for Flood
           Attenuation in Manitoba's Red River Basin
    • Authors: Samantha Fraser;Joni L. Storie;
      Pages: 3578 - 3587
      Abstract: This study builds upon the Environmental Protection Agency (EPA) framework developed for the USA portion of the Red River basin to prioritize potential wetland locations for flood mitigation. The framework was modified to use Canadian data applied on a regional scale, and to address an acknowledged gap in information by including a “Storage Potential” geoindicator based upon soil moisture extracted from RADAR data. This framework was chosen because it reflects all geoindicators found in literature, includes both economic and hydrological indicators, it is mathematically robust, and allows for the potential future comparison between the Canadian and USA portions of the Red River basin. The inclusion of the storage potential indicator reduced the amount of land area identified as suitable for wetland conversion compared to results without the added indicator. However, the cost of land and proximity to streams were the most important indicators in site selection with runoff and storage potential discriminating sites within the low valued lands close to streams. South of Emerson, 0.8% of the land area is needed to reduce the impact of a 100-year flood; this resulted in a small area needed for flood mitigation, thus allowing for flexibility in site suitability which will appeal to land owners, flood managers and Regional Municipality administration. The results showed that this framework can be used in southern Manitoba and can be applied on a regional scale. The next step is to use this framework and data throughout the entire Manitoba Red River basin to mitigate floods on a synoptic scale for comparison with the USA portion of the basin.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series
           Data and Its Validation Based on Ground Measurements
    • Authors: Liying Geng;Mingguo Ma;Haibo Wang;
      Pages: 3588 - 3597
      Abstract: In this study, a compound technique was developed using eight denoising techniques for reconstructing high-quality normalized difference vegetation index (NDVI) time series data. The new algorithm consists of two major procedures: 1) detecting noisy data according to variation in the modification rates of eight selected denoising techniques and 2) using the medians of the denoised values of the eight techniques to replace the noisy data. The eight techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky–Golay (S–G) technique, the mean value iteration (MVI) filter, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight (CW) filter, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. The technique was tested with moderate resolution imaging spectroradiometer (MODIS) NDVI time series data derived from MOD09GQ of the Heihe River Basin in China. In situ NDVI data were obtained during one nearly complete growing season for six land-use types in the study area. Analysis of the temporal and spatial characteristics of the reconstructed data revealed that the compound technique performs better than the other techniques. In addition, the lower root-mean-square error (RMSE) of the compound technique, which was calculated using ground measurements, demonstrated the improved performance of the new technique. The main advantage of the new technique is its ability to effectively denoise data and maintain fidelity such that it can be widely used for other NDVI time series data and for other study areas.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Estimating Site Index From Short-Term TanDEM-X Canopy Height Models
    • Authors: Henrik J. Persson;Johan E. S. Fransson;
      Pages: 3598 - 3606
      Abstract: The tree height growth from three vegetation seasons was fitted to height growth curves in order to estimate the site index, which is a variable related to forest site productivity. The tree height growth was evaluated for four different cases, in which remote sensing data from TanDEM-X and airborne laser scanning were used. The used method requires a digital terrain model and knowledge about the tree species. Furthermore, the remote sensing data were calibrated using Lorey's mean height heights or airborne laser scanning data. It was found that four annual acquisitions of calibrated TanDEM-X data covering three vegetation seasons could be used for estimating the site index on 27 0.5-ha field plots with 4.4-m (12.1%) RMSE. The site index could in a similar manner be estimated from only two airborne laser scanning acquisitions, before and after four vegetation seasons, with 2.3-m (6.3%) RMSE.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Exploring the Validity of the Long-Term Data Record V4 Database for Land
           Surface Monitoring
    • Pages: 3607 - 3614
      Abstract: A new version of the long-term data record (LTDR)—Version 4—has been released recently by NASA. This database includes daily information for all advanced very high resolution radiometer channels, as well as ancillary data, from July 1981 up to present. This dataset is the longest available record of remotely sensed data useful for land surface monitoring, since it allows the daily estimation of vegetation indices, as well as the estimation of land surface temperature (LST). Here, we analyze the fitness of this database for land surface monitoring, especially as regards long-term trends and their validity. To that end, we estimated normalized difference vegetation index (NDVI), LST, as well as extracted solar zenith angle (SZA) from the ancillary data. Then, we reconstructed the yearly temporal profiles of NDVI and LST using the iterative interpolation for data reconstruction approach, from which we extracted parameters such as minimum and maximum values and corresponding dates, as well as the dates of mid-amplitude crossing. We also retrieved SZA values at all mentioned dates. In the following step, we checked for the presence and estimated values for trends using the Mann–Kendall statistical framework for retrieved dates as well as for minimum and maximum values. We then compared the retrieved trends and values to previous results as well as to independent data. As a conclusion, the LTDR-V4 dataset seems adequate for regional-to-global land surface monitoring, provided that time-series reconstruction techniques and orbital drift correction methods are applied.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Cloud Removal in Image Time Series Through Sparse Reconstruction From
           Random Measurements
    • Pages: 3615 - 3628
      Abstract: In this paper, we propose a cloud removal algorithm for scenes within a satellite image time series based on synthetization of the affected areas via sparse reconstruction. The high spectrotemporal dimensionality of time series allows applying pixel-based sparse reconstruction techniques efficiently, estimating the values below a cloudy area by observing the spectral evolution in time of pixels in cloud-free areas. The process implicitly compensates the overall atmospheric interactions affecting a given image, and it is possible even if only one acquisition is available for a given period of time. The dictionary, on the basis of which the data are reconstructed, is selected randomly from the available image elements in the time series. This increases the degree of automation of the process, if the area containing clouds and their shadows is given. Favorable comparisons with similar methods and applications to supervised classification and change detection show that the proposed algorithm restores images locally contaminated by clouds and their shadows in a satisfactory and efficient way.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Patch Matching-Based Multitemporal Group Sparse Representation for the
           Missing Information Reconstruction of Remote-Sensing Images
    • Authors: Xinghua Li;Huanfeng Shen;Huifang Li;Liangpei Zhang;
      Pages: 3629 - 3641
      Abstract: Poor weather conditions and/or sensor failure always lead to inevitable information loss for remote-sensing images acquired by passive sensor platforms. This common issue makes the interpretation (e.g., target recognition, classification, change detection) of remote-sensing data more difficult. Toward this end, this paper proposes to reconstruct the missing information of optical remote-sensing data by patch matching-based multitemporal group sparse representation (PM-MTGSR). In the framework of sparse representation, the basic idea is to utilize the local correlations in the temporal domain and the nonlocal correlations in the spatial domain. Based on image patches, the local correlations are first taken into consideration. The similar patches are then grouped for joint sparse representation so that the nonlocal correlations are also considered. Owing to the patch matching of similar patches, the nonlocal correlations in the remote-sensing images are efficiently exploited. Simulated and real-data experiments demonstrate that the proposed method is effective both qualitatively and quantitatively.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Spatio–Temporal Clustering and Active Learning for Change Classification
           in Satellite Image Time Series
    • Pages: 3642 - 3650
      Abstract: Active learning (AL) has emerged as a versatile approach to reduce the training data required for remote sensing image classification, but its use for the analysis of satellite image time series (SITS) has not been explored yet. This study targets to explore a new object-based framework for change detection in SITS, combining the state-of-the-art spatio–temporal clustering and the use of different machine learning algorithms. Indeed, this study aims at testing whether standard machine learning algorithms can detect changes in long time series and whether AL can improve the results compared to traditional supervised learning. The tested AL algorithms comprise random forest-based heuristics that use a combination of uncertainty and diversity criteria, and a classical SVM breaking ties heuristic. The different implementations are evaluated with two datasets that depict changes around the Arcachon basin (West of France) and around the city of Colmar (East of France) spanning over more than 20 years and comprising four change classes (urban sprawl, forest gain, forest loss, and other). The tests demonstrate that steeper learning curves can be obtained with AL when compared to supervised learning. However, the performance of different AL algorithms depends on the dataset.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Synergy Between LiDAR, RADARSAT-2, and Spot-5 Images for the Detection and
           Mapping of Wetland Vegetation in the Danube Delta
    • Pages: 3651 - 3666
      Abstract: Wetlands are among the most productive natural environments on Earth, as they harbor exceptional biological diversity. For this paper, our study site was the Danube Delta. The biodiversity of the Danube Delta is extraordinary and it possesses one of the largest reed beds in the world. The main goal of our paper was to recognize, characterize, and map the main vegetation units of the Danube Delta. The paper emphasizes the importance of the joint use of LiDAR measurements (acquired in May 2011), RADARSAT-2 radar data (acquired on June 4, 2011), and SPOT-5 optical data (acquired on May 25, 2011). LiDAR data allow for the characterization of vegetation height within centimeter accuracy (10 cm). The radar measurements are based on C-band, providing additional information about the structure of the vegetation cover. The simultaneous acquisition of HH, HV, VV, and VH polarizations enabled us to discriminate between the targets, depending on their responses to the various polarizations, by calculating their polarimetric signatures. By linking multispectral LiDAR and radar data, information can be obtained about vegetation reflectance and height as well as the backscattering mechanism, allowing for improved mapping and characterization accuracy (90.60% mean accuracy). An accuracy assessment of the classification results was evaluated against the vegetation data recorded in the field.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Error Sources in Deforestation Detection Using BFAST Monitor on Landsat
           Time Series Across Three Tropical Sites
    • Authors: Michael Schultz;Jan Verbesselt;Valerio Avitabile;Carlos Souza;Martin Herold;
      Pages: 3667 - 3679
      Abstract: Accurate tropic deforestation monitoring using time series requires methods which can capture gradual to abrupt changes and can account for site-specific properties of the environment and the available data. The generic time series algorithm BFAST Monitor was tested using Landsat time series at three tropical sites. We evaluated the importance of how specific effects of site and radiometric correction affected the accuracy of deforestation monitoring when using BFAST Monitor. Twelve sets of time series of normalized difference vegetation index (NDVI) Landsat data (2000–2013) were analyzed. Time series properties varied according to site (Brazil, Ethiopia, and Vietnam) and which correction scheme was applied: Atmospheric Correction and Haze Reduction 2 and 3 (ATCOR 2 and 3), Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), or Dark Object Subtraction (DOS). Mapping accuracy was compared using 1200 reference points per site and consistent designs for sampling, analysis (overall accuracy, user’s accuracy, and producer’s accuracy), and response (ground truth and very-high-resolution data). With the exception of DOS, mapping accuracies across correction methods were found to be similar but varied greatly with site. Mapping errors were modeled using a set of error parameters that yielded information on data and site-specific environmental properties. Important parameters for characterizing mapping errors were found to be variance of the NDVI and soil signal as well as availability of time series data, and forest edge effects. Based upon the results, local fine-tuning of the algorithm is essential for some areas but for others default settings create satisfactory accuracies.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Multitemporal Analysis of High-Spatial-Resolution Optical Satellite
           Imagery for Mangrove Species Mapping in Bali, Indonesia
    • Pages: 3680 - 3686
      Abstract: Mapping zonations of mangrove species (ZMS) is important when assessing the functioning of such specific ecosystems. However, the reproducibility of remote sensing methods for discriminating and mapping mangrove habitats is often overstated due to the lack of temporal observations. Here, we investigated the potential use of temporal series of high-resolution multispectral satellite images to discriminate and map four typical Asian ZMS. This study was based on the analysis of eight images acquired between 2001 and 2014 over the mangrove area of Nusa Lembongan, Bali, Indonesia. Variations between years in the top-of-atmosphere reflectance signatures were examined as functions of the acquisition angles. We also applied maximum likelihood supervised classification to all of the images and determined the variability in the classification errors. We found that the distinction between spectral signatures of ZMS characterized by a close canopy was fairly independent of the season and sensor characteristics. By contrast, the variability in the multispectral signatures of ZMS with open canopies and associated classification errors could be attributed to variability in ground surface scattering. In both cases, sun-viewing geometry could alter the separability between ZMS classes in near-nadir viewing or frontward sun-viewing configurations, thereby explaining why the overall accuracy of ZMS classification might vary from 65% to 80%. Thus, multitemporal analysis is an important stage in the development of robust methods for ZMS mapping. It must be supported by physical-based research aiming to quantify the influences of canopy structure, species composition, ground surface properties, and viewing geometry parameters on ZMS multispectral signatures.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Monitoring of Agricultural Grasslands With Time Series of X-Band
           Repeat-Pass Interferometric SAR
    • Authors: Karlis Zalite;Oleg Antropov;Jaan Praks;Kaupo Voormansik;Mart Noorma;
      Pages: 3687 - 3697
      Abstract: This study evaluates the potential of X-band interferometry for monitoring of agricultural grasslands. Time series of HH-polarization COSMO-SkyMed 1-day repeat-pass interferometric SAR (InSAR) pairs is analyzed in regard to detecting mowing events, and assessing vegetation height and biomass on grasslands. The time series of four InSAR pairs was analyzed in regard to the ground reference data collected during an extensive campaign covering 11 agricultural grasslands. The calculated temporal interferometric coherence was found to be inversely correlated to the vegetation height and wet above-ground biomass. It was found that grass removal increases the coherence magnitude indicating a potential use of this parameter for the detection of mowing. However, precipitation and farming activity between the acquisitions interfere with this effect. Temporal coherence was expressed as a function of the vegetation height through the random motion of scatterers in the vegetation layer. For vegetation height limited to the range between 0 and 1 m, a very strong correlation between the grass height and the linearised temporal coherence was found, with a coefficient r=text{0.81} . No significant correlation was found between the backscattering coefficient and the wet above-ground biomass as well as the height of grass. However, a strong negative correlation was found between the backscattering coefficient and the measured soil moisture.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Regional Glacier Mapping Using Optical Satellite Data Time Series
    • Pages: 3698 - 3711
      Abstract: The first of two Sentinel-2 satellites, launched mid2015, has similar characteristics as the Landsat TM/ETM + /OLI satellites. Together, these satellites will produce a tremendous quantity of optical images worldwide for glacier mapping, with increasing temporal coverage toward the more glacierized higher latitudes due to convergence of near-polar orbits. To exploit the potential of such near-future dense time series, methods for mapping glaciers from space should be revisited. Currently, snow and ice are typically classified from an optical satellite image using a multispectral band ratio. For each scene, mapping conditions will vary (e.g., snow, ice, and clouds) and not be equally optimal over the entire scene. The increasing amount of images makes it difficult to manually select the best glacier mapping scene as is the current practice. This work is based on the above robust image ratio method for exploiting the dense temporal image coverage. Four application scenarios using time series of Landsat type data for glacier mapping are presented. First, we synthesize an optimal band ratio image from a stack of images within one season to compensate for regional differences. The second application scenario introduces robust methods to improve automatic glacier mapping by exploiting the seasonal variation in spectral properties of snow. Third, we explore the spatio-temporal variation of glacier surface types. Finally, we show how the synthesized band ratio images from the first application scenario can be used for automatic glacier change detection. In summary, we explore automatic algorithms for glacier mapping applications that exploit the temporal signatures in the satellite data time series.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and
           Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in
           Ukraine
    • Authors: Sergii Skakun;Nataliia Kussul;Andrii Yu. Shelestov;Mykola Lavreniuk;Olga Kussul;
      Pages: 3712 - 3719
      Abstract: Ukraine is one of the most developed agricultural countries in the world. For many applications, it is extremely important to provide reliable crop maps taking into account diversity of cropping systems used in Ukraine. The use of optical imagery only is limited due to cloud cover, and previous studies showed particular difficulties in discriminating summer crops in Ukraine such as maize, soybeans, sunflower, and sugar beet. This paper focuses on exploring feasibility and assessing efficiency of using multitemporal satellite synthetic-aperture radar (SAR) acquired in C-band and optical images for crop classification in Ukraine. Both optical (Landsat-8/OLI) and SAR (Radarsat-2) images are used to assess the impact of adding backscattering intensity from SAR images for classification purposes. SAR intensity information is very important due to availability of Sentinel-1 imagery over Ukraine starting March 2015. Different combinations of optical and SAR images, as well as SAR modes and polarizations, are assessed for better discrimination of crops. A committee of neural networks, in particular multilayer perceptrons (MLPs), is used to improve classification accuracy compared to several standard classifiers. It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images. Considering the summer crops, the major impact of adding backscatter intensity information from SAR images is in better separation of sunflower, soybeans, and maize.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • A Spatio-Temporal Data-Mining Approach for Identification of Potential
           Fishing Zones Based on Oceanographic Characteristics in the Eastern Indian
           Ocean
    • Authors: Devi Fitrianah;Achmad Nizar Hidayanto;Jonson Lumban Gaol;Hisyam Fahmi;Aniati Murni Arymurthy;
      Pages: 3720 - 3728
      Abstract: The traditional approach for determining potential fishing zones (PFZs) relies on oceanographic factors (biological, physical, and chemical) and fishermen's expertise. This approach has disadvantages particularly when it comes to the analysis of combining these factors to find an exact PFZ spatially and temporally. In this study, we proposed a framework for identifying PFZs based on a data-mining approach in the Eastern Indian Ocean. We utilized a spatio-temporal clustering method to identify clusters of zones with data on the largest number of fish catch, which were then integrated with the sea surface temperature (SST) and the sea surface chlorophyll a (SSC) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The results of this data integration method were used as training data in the classification process, which was then used to determine PFZs. During the classification process, we utilized the $k$-nearest neighbor (KNN) classification method. The result gave an average accuracy of 87.11%, which showed that the proposed framework can be used effectively to determine PFZs. To validate the framework, we compared its performance against the heuristic rules taken from the knowledge-based expert system model on the SST and chlorophyll a data. The results showed that the proposed data-mining framework outperformed the heuristic rules from the knowledge-based expert system model.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover
           Mapping
    • Pages: 3729 - 3739
      Abstract: This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Urban Areas Enhancement in Multitemporal SAR RGB Images Using Adaptive
           Coherence Window and Texture Information
    • Authors: Donato Amitrano;Veronica Belfiore;Francesca Cecinati;Gerardo Di Martino;Antonio Iodice;Pierre-Philippe Mathieu;Stefano Medagli;Davod Poreh;Daniele Riccio;Giuseppe Ruello;
      Pages: 3740 - 3752
      Abstract: In this paper, we present a technique for improving the representation of built-up features in model-based multitemporal synthetic aperture radar (SAR) RGB composites. The proposed technique exploits the multitemporal adaptive processing (MAP3) framework to generate an a priori information which is used to implement an adaptive selection of the coherence window size. Image texture is used to support the coherence information in case of decorrelation. The coherence information, powered by texture analysis, and combined with backscattering amplitude, provides a unique representation of built-up features. This allows for an immediate detection of urban agglomerates by human operators, and is an advantaged starting point for urban area extraction algorithms.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • ++Radiance:+Effects+of+Time+of+Year,+Latitude,+Land+Cover,+and+View+Zenith+Angle&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&rft.date=2016&rft.volume=9&rft.spage=3753&rft.epage=3760&rft.aulast=Henebry;&rft.aufirst=Monika&rft.au=Monika+Tomaszewska;Valeriy+Kovalskyy;Christopher+Small;Geoffrey+M.+Henebry;">Viewing Global Megacities Through MODIS Radiance: Effects of Time of Year,
           Latitude, Land Cover, and View Zenith Angle
    • Authors: Monika Tomaszewska;Valeriy Kovalskyy;Christopher Small;Geoffrey M. Henebry;
      Pages: 3753 - 3760
      Abstract: Cities are often obscured by haze and smoke when viewed in the visible and near infrared, but the longer wavelengths of the middle infrared can penetrate fine aerosol layers. We characterized variation in 4-μm radiance in and nearby eight global megacities using MODIS band 23 calibrated radiance. The seasonality of middle infrared (MIR) radiance was more pronounced at higher latitudes. Precipitation attenuated MIR radiance. The seasonality of MIR radiance from exposed soils was very similar to urban surfaces, complicating discrimination. The variety of urban surfaces across the megacities also affected the seasonality of MIR radiance. Additional data and MIR radiance with higher spatial resolution could improve and refine/detail information about MIR radiance behavior for further investigation.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • The Interferometric Use of Radar Sensors for the Urban Monitoring of
           Structural Vibrations and Surface Displacements
    • Authors: Antonio Montuori;Guido Luzi;Christian Bignami;Iolanda Gaudiosi;Salvatore Stramondo;Michele Crosetto;Maria Fabrizia Buongiorno;
      Pages: 3761 - 3776
      Abstract: In this paper, we propose a combined use of real aperture radar (RAR) and synthetic aperture radar (SAR) sensors, within an interferometric processing chain, to provide a new methodology for monitoring urban environment and historical buildings at different temporal and spatial scales. In particular, ground-based RAR measurements are performed to estimate the vibration displacements and the natural oscillation frequencies of structures, with the aim of supporting the understanding of the building dynamic response. These measurements are then juxtaposed with ground-based and space-borne SAR data to monitor surface deformation phenomena, and hence, point out potential risks within an urban environment. In this framework, differential interferometric SAR algorithms are implemented to generate short-term (monthly) surface displacement and long-term (annual) mean surface displacement velocity maps at local (hundreds m2) and regional (tens km2) scale, respectively. The proposed methodology, developed among the activities carried out within the national project Programma Operativo Nazionale MASSIMO (Monitoraggio in Area Sismica di SIstemi MOnumentali), is tested and discussed for the ancient structure of Saint Augustine compound, located in the historical center of Cosenza (Italy) and representing a typical example of the Italian Cultural Heritage.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Simulation-Based Building Change Detection From Multiangle SAR Images and
           Digital Surface Models
    • Authors: Junyi Tao;Stefan Auer;
      Pages: 3777 - 3791
      Abstract: This paper presents two change-detection strategies based on the fusion of scene knowledge and two high-resolution synthetic aperture radar (SAR) images (pre-event, postevent) with focus on individual buildings and facades. Avoiding the dependence of the signal incidence angle, the methods increase the flexibility with respect to near-real-time SAR image analysis after unexpected events. Knowledge of the scene geometry is provided by digital surface models (DSMs), which are integrated into an automated simulation processing chain. Using strategy 1 (based on building fill ratio, BFR), building changes are detected based on change ratios considering layover and shadow areas. Strategy 2 (based on wall fill position, WFP) enables one to analyze individual facades of buildings without clear decision from strategy 1, which is based on a geometric projection of facade layover pixels. In a case study (Munich city center), the sensitivity of the change-detection methods is exemplified with respect to destroyed buildings and partly changed buildings. The results confirm the significance of integrating prior knowledge from DSMs into the analysis of high-resolution SAR images.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Building Collapse Assessment in Urban Areas Using Texture Information From
           Postevent SAR Data
    • Authors: Weidong Sun;Lei Shi;Jie Yang;Pingxiang Li;
      Pages: 3792 - 3808
      Abstract: A number of earthquakes have occurred in recent years, posing a challenge to Earth observation techniques. Owing to the all-weather response capability, synthetic aperture radar (SAR) has become a key tool for collapse interpretation. As the requirement for multitemporal data is usually not satisfied in practice, interpretation using only postevent SAR imagery is indispensable for emergency rescue. Despite being found that texture has a relationship with building damage, only a few texture measures have been adopted using simple threshold criteria. To fully explore the spatial contextual information in very high resolution (VHR) SAR images, there are two questions that should be discussed: 1) Which textural features are helpful for collapse assessment? 2) How do diverse imaging configurations influence the interpretation? In response, five texture descriptors are used for the stricken area texture extraction in this paper, and a random forests classifier is applied to identify the building collapse level. It was found that most of the gray-level histogram features perform quite well, and several other primitive features, such as the isolated bright points originating from scattered rubble, can also help to discriminate different collapse levels. Moreover, the experiments with data from the Yushu earthquake of April 14, 2010, indicated that spatial detail quality is the key for texture interpretation, and the span image can be considered as an appropriate choice when VHR PolSAR data are available. The optimal interpretation results (with an overall accuracy of 84.7%) were obtained using the 122 proposed measures in a VHR X-band image.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • A Temporal Estimation of Entropy and Its Comparison With Spatial
           Estimations on PolSAR Images
    • Pages: 3809 - 3820
      Abstract: Most of the applications of SAR polarimetry such as classification are based on estimation of the polarimetric covariance matrix. This estimation is generally done through a boxcar spatial filtering. This estimation process can induce mixture if different scatterers are present in neighboring pixels. Since the polarimetric entropy {H} is a measure of variability, this mixture can result in a very uniform entropy map. A nonlocal algorithm can be used to improve the estimation of the covariance matrices. The entropy maps are smoothed and contrast is better preserved. We propose a third estimation of {H} by using a temporal stack. Pixels are averaged on the time axis instead of on a spatial basis. On the datasets we studied, the temporal estimation increases the contrast of {H} maps. This contrast allows us to better discriminate targets. Temporal entropy is very influenced by the degree of coherence. Nevertheless, {H}_{text{te\mporal}} provides additional information, combining information about the polarimetric stability of scattering mechanisms over time.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Application of the Variational-Mode Decomposition for Seismic
           Time–frequency Analysis
    • Authors: Ya-Juan Xue;Jun-Xing Cao;Da-Xing Wang;Hao-Kun Du;Yao Yao;
      Pages: 3821 - 3831
      Abstract: Seismic time-frequency analysis methods play an important role in seismic interpretation for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. Variational-mode decomposition (VMD) is a newly developed methodology for decomposition on adaptive and quasi-orthogonal signal and can decompose a seismic signal into a number of band-limited quasi-orthogonal intrinsic mode functions (IMFs). Each mode is an AM-FM signal with the narrow-band property and nonnegative smoothly varying instantaneous frequencies. Analysis on synthetic and real data shows that this method is more robust to noise and has stronger local decomposition ability than the empirical mode decomposition (EMD)-based methods. Comparing with the short-time Fourier transform (STFT) or wavelet transform (WT), instantaneous spectrum after VMD promises higher spectral and spatial resolution. Application of the VMD on field data demonstrates that instantaneous spectrum after VMD targets the thickness variation in the coal seam more sensitively than the conventional tools and highlights the fine details that might escape unnoticed. The technique is more promising for seismic signal processing and interpretation.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • TOPS Time Series Performance Assessment With TerraSAR-X Data
    • Authors: Matteo Nannini;Pau Prats-Iraola;Francesco De Zan;Dirk Geudtner;
      Pages: 3832 - 3848
      Abstract: The terrain observation by progressive scans (i.e., TOPS) SAR mode enables a large ground coverage with an azimuth-invariant SNR, while avoiding the drawbacks of scalloping. The implementation of TOPS, obtained by steering the antenna from aft to fore within a burst, introduces an azimuth-dependent Doppler variation on the received signal. The processing of TOPS data needs to account for this signal property, requiring additional care especially when used for SAR interferometry (InSAR) applications. In particular, achieving a high coregistration accuracy is more stringent than for stripmap modes. For the analysis of TOPS InSAR data time series, the required high coregistration accuracy is even more critical, because any small error would translate into a biased estimation of geophysical parameters, such as the surface deformation. In this context, the interferometric processing aspects related to TOPS are discussed in this paper, and several analyses to evaluate the TOPS performance when processing time series are presented. Both permanent scatterers (PS) and small baselines subset (SBAS) techniques will be employed to perform the analysis. Two interleaved time series acquired by the TerraSAR-X sensor in TOPS and stripmap modes are used to evaluate and compare the results.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Active Microwave Scattering Signature of Snowpack—Continuous Multiyear
           SnowScat Observation Experiments
    • Pages: 3849 - 3869
      Abstract: European Space Agency's SnowScat instrument is a real aperture scatterometer which was developed by Gamma Remote Sensing AG. It operates in a continuous-wave mode, covers a frequency range of 9.15-17.9 GHz in a user-defined frequency-step and has a full polarimetric capability. The measurement campaigns were started first in February 2009 at Weissfluhjoch, in Davos, Switzerland, as an initial test of the instrument over a deep alpine snowpack. Physical characterizations of the snowpack and meteorological measurements were carried out, which formed a detailed in situ dataset. SnowScat was then moved to Sodankylä in Finland in early November 2009, a site of the Finnish Meteorological Institute in Lapland. In addition to the in situ snowpack characterizations and meteorological observations, continuous passive microwave observations were also performed. During the 2012-2013 winter period, a vertical time-domain snow profiling experiment was carried out in addition for resolving the scattering contributions from the snow layers of different physical properties. This paper summarizes the results of the SnowScat observations and initial comparisons against the in situ meteorological and snowpack data. The Sodankylä campaign data evidenced the high variability of the radar backscatter behavior of snowpack from year to year, which indicates its strong dependency on changing snow microstructure. Indeed, the snow microstructure is continuously driven by snow metamorphism, which are further affected by meteorological conditions and their interannual variability. The backscattering property of snowpack in the range X- to Ku-band for all polarizations appeared to be dominated by its microstructural morphology and underlying ground conditions, and to a lesser extent by the snow depth, or its snow-water-equivalent.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Elevation Changes Inferred From TanDEM-X Data Over the Mont-Blanc Area:
           Impact of the X-Band Interferometric Bias
    • Pages: 3870 - 3882
      Abstract: The TanDEM-X mission allows generation of digital elevation models (DEMs) with high potential for glacier monitoring, but the radar penetration into snow and ice remains a main source of uncertainty. In this study, we generate five new DEMs of the Mont-Blanc area from TanDEM-X interferometric pairs acquired in 2012/2013. We conducted a multitemporal analysis of the DEMs in comparison with two high-resolution DEMs obtained from Pléiades stereo satellite images in 2012 and 2013. A vertical precision of 1–3 m of the radar DEMs is estimated over ice and snow free areas and slopes less than 40°. DEM-derived elevation changes are compared with outputs of the snowpack model Crocus and snow accumulation measurements. The results show that at altitudes below \sim 2500-m a.s.l., the radar penetration is negligible in our study area. The DEM-derived elevation changes agree, within uncertainty, with the modeled and field snow height. At higher altitudes, the comparison between the radar and optical DEMs acquired only a few weeks apart allows estimating the interferometric bias of the X-band DEM in the dry snowpack. At 4000-m a.s.l, it reaches 4 m on average in October and February. A geodetic glacier mass balance calculated using the October radar DEM would be biased. For the least favorable case, the highly elevated Bossons glacier, the bias would correspond to 1.66-m w.e. This error is too large to derive significant annual mass balances, but similar to elevation or seasonality uncertainties if integrated over a 10-years period.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • A Minimum Acceleration Approach for the Retrieval of Multiplatform InSAR
           Deformation Time Series
    • Pages: 3883 - 3898
      Abstract: We present in this paper a technique for the generation of 3-D (2-D) displacement time series of the earth's surface, based on the combination of multiplatform SAR data. The algorithm assumes the availability of two (or more) archives of SAR images acquired from complementary (i.e., ascending/descending) tracks over the same area on the ground. SAR data are preprocessed through one of the currently available multitemporal differential interferometry synthetic aperture radar (DInSAR) toolboxes in order to recover, in correspondence to a set of very coherent points, the line-of-sight (LOS) displacement time series. The latter are then geocoded to a common grid and jointly inverted (pixel-by-pixel) to estimate the (unknown) time series of the 3-D (East-West, North-South, Up-Down) displacement components. To this aim, an underdetermined system of linear equations has to be solved. Previous works have proposed to solve similar ill-posed problems by applying the (truncated) singular-value-decomposition method and/or by regularizing the germane system of linear equations by adding further constraints, which impose conditions on the minimum-norm velocity of the solution. On the contrary, in this study, we adopt a different strategy, which is based on imposing that the 3-D deformation time series have minimum acceleration. The developed combination technique is a postprocessing tool that can be easily implemented. Indeed, it does not require the simultaneous processing of very large sequences of DInSAR interferograms. As a matter of fact, the retrieval of preliminary LOS-projected DInSAR time series can be independently carried out by using one (or more) of the currently available multitemporal DInSAR toolboxes, with no restrictions at all on the class to which they belong (small-baseline- and/or permanent-scatterers-oriented). Experiments carried out on simulated and real data prove the validity of the proposed combination algorithm in retrieving 2-D (Ea-t-West, Up-Down) surface displacement time series with subcentimeter accuracy, and the North-South components with an accuracy of some centimeters.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Proceedings of the IEEE
    • Pages: 3899 - 3899
      Abstract: Advertisement, IEEE.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
  • Introducing IEEE collabratec
    • Pages: 3900 - 3900
      Abstract: Advertisement, IEEE.
      PubDate: Aug. 2016
      Issue No: Vol. 9, No. 8 (2016)
       
 
 
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