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  Subjects -> ELECTRONICS (Total: 152 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 5)
Advances in Microelectronic Engineering     Open Access   (Followers: 1)
Advances in Power Electronics     Open Access   (Followers: 8)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 59)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 10)
Annals of Telecommunications     Hybrid Journal   (Followers: 4)
APL : Organic Electronics and Photonics     Hybrid Journal   (Followers: 2)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 6)
Archives of Electrical Engineering     Open Access   (Followers: 9)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 8)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 15)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Biomedical Instrumentation & Technology     Full-text available via subscription   (Followers: 4)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 5)
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: 13)
China Communications     Full-text available via subscription   (Followers: 2)
Circuits and Systems     Open Access   (Followers: 9)
Consumer Electronics Times     Open Access   (Followers: 4)
Control Systems     Hybrid Journal   (Followers: 21)
Electronic Markets     Hybrid Journal   (Followers: 6)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 7)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics Letters     Hybrid Journal   (Followers: 17)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 18)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 8)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and TrendsĀ® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and TrendsĀ® in Signal Processing     Full-text available via subscription   (Followers: 4)
Frequenz     Full-text available via subscription   (Followers: 2)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 2)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 20)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 14)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 13)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 11)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 10)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 2)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 5)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 10)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 12)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 22)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 13)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 6)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 13)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 15)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 8)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 7)
IET Power Electronics     Hybrid Journal   (Followers: 7)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 6)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 11)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Informatik-Spektrum     Hybrid Journal  
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 1)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 5)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 20)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 3)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 11)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 1)
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 6)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 2)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 4)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 3)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 1)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 4)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 4)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 2)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 8)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 11)
International Transaction of Electrical and Computer Engineers System     Open Access  
ISRN Electronics     Open Access   (Followers: 1)
ISRN Signal Processing     Open Access  
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 2)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Full-text available via subscription   (Followers: 1)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 4)

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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     [SJR: 1.232]   [H-I: 14]
   [18 followers]  Follow    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [175 journals]
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Pages: C4 - C4
      Abstract: Advertisement.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing Information for Authors
    • Pages: C3 - C3
      Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • [Front cover]
    • Pages: C1 - C1
      Abstract: Presents the front cover for this issue of the publication.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • Pages: C2 - C2
      Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Table of contents
    • Pages: 4613 - 4614
      Abstract: Presents the table of contents for this issue of this publication.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Foreword to the Special Issue on Pattern Recognition in Remote Sensing
    • Authors: Du; Q.;Michaelsen, E.;Du, P.;Bruzzone, L.;Tong, X.;Stilla, U.;
      Pages: 4615 - 4619
      Abstract: The articles in this special section focus on the use of pattern recognition in remote sensing applications.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Land-Use Scene Classification Using a Concentric Circle-Structured
           Multiscale Bag-of-Visual-Words Model
    • Authors: Zhao; L.;Tang, P.;Huo, L.;
      Pages: 4620 - 4631
      Abstract: High-resolution remote sensing image-based land-use scene classification is a difficult task, which is to recognize the semantic category of a given land-use scene image based on priori knowledge. Land-use scenes often cover multiple land-cover classes or ground objects, which makes a scene very complex and difficult to represent and recognize. To deal with this problem, this paper applies the well-known bag-of-visual-words (BOVWs) model which has been very successful in natural image scene classification. Moreover, many existing BOVW methods only use scale-invariant feature transform (SIFT) features to construct visual vocabularies, lacking in investigation of other features or feature combinations, and they are also sensitive to the rotation of image scenes. Therefore, this paper presents a concentric circle-based spatial-rotation-invariant representation strategy for describing spatial information of visual words and proposes a concentric circle-structured multiscale BOVW method using multiple features for land-use scene classification. Experiments on public land-use scene classification datasets demonstrate that the proposed method is superior to many existing BOVW methods and is very suitable to solve the land-use scene classification problem.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Efficient and Effective Hierarchical Feature Propagation
    • Authors: dos Santos; J.A.;Penatti, O.A.B.;Gosselin, P.;Falcao, A.X.;Philipp-Foliguet, S.;Torres, R.d.S.;
      Pages: 4632 - 4643
      Abstract: Many methods have been recently proposed to deal with the large amount of data provided by the new remote sensing technologies. Several of those methods rely on the use of segmented regions. However, a common issue in region-based applications is the definition of the appropriate representation scale of the data, a problem usually addressed by exploiting multiple scales of segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting features from a hierarchy by proposing two approaches that exploit the existing relationships among regions at different scales. The H-Propagation propagates any histogram-based low-level descriptors. The bag-of-visual-word (BoW)-Propagation approach uses the BoWs model to propagate features along multiple scales. The proposed methods are very efficient, as features need to be extracted only at the base of the hierarchy and yield comparable results to low-level extraction approaches.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Morphological Profiles Based on Differently Shaped Structuring Elements
           for Classification of Images With Very High Spatial Resolution
    • Authors: Lv; Z.Y.;Zhang, P.;Benediktsson, J.A.;Shi, W.Z.;
      Pages: 4644 - 4652
      Abstract: Morphological profiles (MPs) have been proposed for the segmentation and classification of high spatial resolution (HSR) images. A shortcoming of the originally proposed MPs is that the profiles were only based on structuring elements (SEs) of one particular shape, suggesting that such MPs may not be suitable for detecting different shapes in images. To better fit several shapes in a given image, a new approach based on mathematical morphology is proposed to extract structural information from HSR images and consequently yield new versions of MPs. The classification results for the new MPs are compared with the classification of spatial features extracted with the use of pixel shape index, gray level co-occurrence matrix, and previously proposed MPs. The experimental results suggest the following: 1) structural and spectral features can complement each other and their integration can improve classification accuracy and 2) MPs constructed by differently shaped SEs are less sensitive to salt-and-pepper noise than those constructed by fixed-shaped SEs.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Multiple Morphological Profiles From Multicomponent-Base Images for
           Hyperspectral Image Classification
    • Authors: Huang; X.;Guan, X.;Benediktsson, J.A.;Zhang, L.;Li, J.;Plaza, A.;Dalla Mura, M.;
      Pages: 4653 - 4669
      Abstract: Morphological profiles (MPs) are a useful tool for remotely sensed image classification. These profiles are constructed on a base image that can be a single band of a multicomponent remote sensing image. Principal component analysis (PCA) has been used to provide other base images to construct MPs in high-dimensional remote sensing scenes such as hyperspectral images [e.g., by deriving the first principal components (PCs) and building the MPs on the first few components]. In this paper, we discuss several strategies for producing the base images for MPs, and further categorize the considered methods into four classes: 1) linear, 2) nonlinear, 3) manifold learning-based, and 4) multilinear transformation-based. It is found that the multilinear PCA (MPCA) is a powerful approach for base image extraction. That is because it is a tensor-based feature representation approach, which is able to simultaneously exploit the spectral–spatial correlation between neighboring pixels. We also show that independent component analysis (ICA) is more effective for constructing base images than PCA. Another important contribution of this paper is a new concept of multiple MPs (MMPs), aimed at synthesizing the spectral–spatial information extracted from the multicomponent base images, and further enhancing the classification accuracy of MPs. Moreover, we propose two different strategies to interpret the newly proposed MMPs by considering their hyperdimensional feature space: 1) decision fusion and 2) sparse classifier based on multinomial logistic regression (MLR). Experiments conducted on three well-known hyperspectral datasets are used to quantitatively assess the accuracy of different algorithms.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Signal Classification for Ground Penetrating Radar Using Sparse Kernel
           Feature Selection
    • Authors: Shao; W.;Bouzerdoum, A.;Phung, S.L.;
      Pages: 4670 - 4680
      Abstract: This paper addresses the problem of feature selection for the classification of ground penetrating radar signals. We propose a new classification approach based on time–frequency analysis and sparse kernel feature selection. In the proposed approach, a time–frequency or a time-scale transform is first applied to the one-dimensional radar trace. Sparse kernel feature selection is then employed to extract an optimum set of features for classification. The sparse kernel method is formulated as an underdetermined linear system in a high-dimensional space, and the category labels of the training samples are used as measurements to select the most informative features. The proposed approach is evaluated through an industrial application of assessing railway ballast fouling conditions. Experimental results show that the proposed combination of sparse kernel feature selection and support vector machine classification yields very high classification rates using only a small number of features.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Influence of Data Source and Training Size on Impervious Surface Areas
           Classification Using VHR Satellite and Aerial Imagery Through an
           Object-Based Approach
    • Authors: Fernandez; I.;Aguilar, F.J.;Aguilar, M.A.;Alvarez, M.F.;
      Pages: 4681 - 4691
      Abstract: Two very-high-resolution (VHR) satellite images from the GeoEye-1 and WorldView-2 sensors have been used in order to extract impervious surface areas (ISAs) over a Mediterranean coastal area of Almeria (Spain) through an object-based image analysis (OBIA). Different feature sets (basic multispectral information, relative spectral indices, and texture indices based on local variance) were used to feed a support vector machine (SVM) classifier in order to determine the most suitable information for ISAs classification. The classification results coming from both satellite images were compared to each other and also against those provided by a previous similar work carried out on an archival orthoimage. An estimation of the most appropriate number of training samples was performed for each data source by a sampling size reduction procedure. The accuracy assessment of the classification results showed that texture based on local variance was a valuable feature to improve ISA classification accuracy. When texture based on variance was included, the classification accuracy results provided by the archival orthoimage experiment (overall accuracy: 88.1% and KHAT: 0.760) were similar to those obtained from the VHR-satellite images (overall accuracy: 90.4% and 89.7%, KHAT: 0.806 and 0.792 for GeoEye-1 and WorldView-2, respectively). Finally, the influence of the data source and training size on ISA classification accuracy was also proved.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Efficient Framework for Palm Tree Detection in UAV Images
    • Authors: Malek; S.;Bazi, Y.;Alajlan, N.;AlHichri, H.;Melgani, F.;
      Pages: 4692 - 4703
      Abstract: The latest developments in unmanned aerial vehicles (UAVs) and associated sensing systems make these platforms increasingly attractive to the remote sensing community. The large amount of spatial details contained in these images opens the door for advanced monitoring applications. In this paper, we use this cost-effective and attractive technology for the automatic detection of palm trees. Given a UAV image acquired over a palm farm, first we extract a set of keypoints using the Scale-invariant Feature Transform (SIFT). Then, we analyze these keypoints with an extreme learning machine (ELM) classifier a priori trained on a set of palm and no-palm keypoints. As output, the ELM classifier will mark each detected palm tree by several keypoints. Then, in order to capture the shape of each tree, we propose to merge these keypoints with an active contour method based on level sets (LSs). Finally, we further analyze the texture of the regions obtained by LS with local binary patterns (LBPs) to distinguish palm trees from other vegetations. Experimental results obtained on UAV images with 3.5 cm of spatial resolution and acquired over two different farms confirm the promising capabilities of the proposed framework.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Regions of Interest Detection in Panchromatic Remote Sensing Images Based
           on Multiscale Feature Fusion
    • Authors: Zhang; L.;Yang, K.;Li, H.;
      Pages: 4704 - 4716
      Abstract: A global searching solution was often employed in traditional prior-knowledge-based regions of interest (ROIs) detection methods for processing high-resolution remote sensing images, which results in prohibitively complex computing. To solve this problem, this study proposes a faster and more efficient ROI detection algorithm based on multiscale feature fusion, wherein the input image is processed along two feature channels: 1) intensity and 2) orientation. The multiscale spectrum residuals method is proposed to compute intensity saliency. The interpolating biorthogonal integer wavelet transform (IB-IWT) is used to extract orientation features, and the orientation saliency is obtained with thresholding and filtering. A weighted across-scale fusion method is proposed to combine conspicuity maps at different scales into one map while retaining salient regions at different scales. The experimental results reveal that the new algorithm is computationally efficient and provides more visually accurate detection results.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Automatic Auroral Detection in Color All-Sky Camera Images
    • Authors: Rao; J.;Partamies, N.;Amariutei, O.;Syrjasuo, M.;van de Sande, K.E.A.;
      Pages: 4717 - 4725
      Abstract: Every winter, the all-sky cameras (ASCs) in the MIRACLE network take images of the night sky at regular intervals of 10–20 s. This amounts to millions of images that not only need to be pruned, but there is also a need for efficient auroral activity detection techniques. In this paper, we describe a method for performing automated classification of ASC images into three mutually exclusive classes: aurora, no aurora, and cloudy. This not only reduces the amount of data to be processed, but also facilitates in building statistical models linking the magnetic fluctuations and auroral activity helping us to get a step closer to forecasting auroral activity. We experimented with different feature extraction techniques coupled with Support Vector Machines classification. Color variants of Scale Invariant Feature Transform (SIFT) features, specifically Opponent SIFT features, were found to perform better than other feature extraction techniques. With Opponent SIFT features, we were able to build a classification model with a cross-validation accuracy of 91%, which was further improved using temporal information and elimination of outliers which makes it accurate enough for operational data pruning purposes. Since the problem is essentially similar to scene detection, local point description features perform better than global- and texture-based feature descriptors.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • River Delineation from Remotely Sensed Imagery Using a Multi-Scale
           Classification Approach
    • Authors: Yang; K.;Li, M.;Liu, Y.;Cheng, L.;Duan, Y.;Zhou, M.;
      Pages: 4726 - 4737
      Abstract: River delineation is an initial yet critical step in river studies. Although the analysis of satellite images shows great potential in river delineation, only a few approaches have been developed. These methods usually focus on rivers at mono-scale and may ignore the large variations in river size. In particular, they may fail to capture the small rivers in the imagery. This paper presents a novel automated multi-scale procedure for delineating complete river networks. This method classifies the large and small rivers separately and combines the two classified results to generate the final delineated river networks. First, a modified normalized difference water index (MNDWI) is adapted to enhance the spectral contrast between open water and land surfaces. Second, a simple OTSU classification is used to delineate the large rivers. Next, a top-hat transformation and multi-scale matched filters are used to enhance the small linear rivers. Then, the OTSU classification is conducted again to delineate the small linear rivers, in concert with a multi-points fast marching method to rejoin the resulting river segments. Finally, the complete river networks are delineated by combining the small and large rivers. A comparison of this procedure with manual digitization when applied to two Landsat-5 TM images demonstrates the former procedure’s value in delineating rivers. It achieves accurate results and outperforms the other three alternative approaches (large river classification, maximum likelihood classifier, and support vector machine classifier) in accuracy, true positive rate, and Kappa coefficient, while also yielding a comparable false positive rate.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Semiautomatic Airport Runway Extraction Using a Line-Finder-Aided Level
           Set Evolution
    • Authors: Li; Z.;Liu, Z.;Shi, W.;
      Pages: 4738 - 4749
      Abstract: In recent years, airport runway extraction has become increasingly important for various engineering applications. Existing approaches for airport runway extraction primarily focus on locating the airport roughly, i.e., determining whether an airport is present or not, but not delineating the airport runway accurately. This study develops a novel method for semiautomatic airport runway extraction from Google earth images by integrating a long straight line finder and a region-based level set evolution (LSE). Specifically, we start by detecting the long straight lines that most likely represent airport runway boundaries in the original images. Then, based on the extracted lines, we propose a method for semiautomatic generation of initial level curves for the LSE. Furthermore, for accurate extraction of the entire airport runways, a fast region-based LSE is used to evolve the initial level curves toward the desired boundaries. Experiments validate that the proposed method is capable of semiautomatically extracting objects with complex geometrical shapes and topological structures from challenging backgrounds. Compared with other state-of-the-art approaches, the proposed method has much fewer parameters and is more computationally efficient while achieving object extraction accuracy comparable to other approaches.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Automated Extraction of 3-D Railway Tracks from Mobile Laser Scanning
           Point Clouds
    • Authors: Yang; B.;Fang, L.;
      Pages: 4750 - 4761
      Abstract: The demand for automated railway tracks extraction is driven by the importance of maintaining and updating the fundamental geographic data of railway tracks for railway engineering. Mobile laser scanning (MLS), which is a promising technology for the rapid 3-D mapping of railways, provides a good means to capture details along the corridors, including tracks, clearance of overhanging wires, natural obstructions (e.g., trees and rock faces), and tunnel/bridge clearances. This paper presents an automated method to detect tracks from MLS point clouds. Both the geometry and intensity data of railway tracks are utilized to extract track points and to model tracks. Experiments were undertaken to evaluate the validity of the proposed method based on the test dataset captured by Optech’s Lynx Mobile Mapper System, proving it a promising solution to extract 3-D tracks from MLS point clouds.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A Method for Accurate Road Centerline Extraction From a Classified Image
    • Authors: Miao; Z.;Wang, B.;Shi, W.;Wu, H.;
      Pages: 4762 - 4771
      Abstract: Accurate road centerline extraction plays an important role in practical remote sensing applications. Most existing centerline extraction methods have many limitations when the classified image contains complicated objects such as curvilinear, close, or short extent features. To cope with these limitations, this study presents a novel accurate centerline extraction method that integrates tensor voting, principal curves, and the geodesic method. The proposed method consists of three main steps. Tensor voting is first used to extract feature points from the classified image. The extracted feature points are then projected onto the principal curves. Finally, the feature points are linked by the geodesic method to create the central line. The experimental results demonstrate that the proposed method, which is automatic, provides a comparatively accurate solution for centerline extraction from a classified image.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A New Region Growing-Based Method for Road Network Extraction and Its
           Application on Different Resolution SAR Images
    • Authors: Lu; P.;Du, K.;Yu, W.;Wang, R.;Deng, Y.;Balz, T.;
      Pages: 4772 - 4783
      Abstract: Road network extraction plays an irreplaceable role in the applications of synthetic aperture radar (SAR) images. In this paper, we propose a new method based on the region growing to quickly extract the road network, which is suitable for different resolution SAR images. First, a weighted ratio line detector (W-RLD) is proposed to extract road features. Then, an automatic road seeds extraction method, which merges the ratio and direction information, is utilized to improve the quality of the extracted road seeds. Finally, the region growing concept is adopted to construct the road network, and a fast parameter selection procedure is presented for adaptively adjusting growing parameters. In experiments, four kinds of SAR images are used to assess the performance of the proposed method, including Envisat ASAR (30 m), HJ-1-C (5 m), TerraSAR-X (3 m), and airborne C-band data (0.5 m). Both visual and quantitative evaluation results show the adaptability and efficiency of the proposed approach.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Remote Sensing Image Super-Resolution Reconstruction Based on Nonlocal
           Pairwise Dictionaries and Double Regularization
    • Authors: Gou; S.;Liu, S.;Yang, S.;Jiao, L.;
      Pages: 4784 - 4792
      Abstract: A nonlocal pairwise dictionary learning (NPDL) model that includes an estimated dictionary and a residual dictionary is applied to remote sensing image super-resolution (SR) reconstruction in this paper. The dictionary pair is trained from some low-resolution (LR) remote sensing images to deal with the lack of high-resolution component in remote sensing images. The reconstructed image has been shown to retain the structural information of the given LR image itself. Moreover, the local and nonlocal (NL) priors are used for image SR to enhance robustness of the pairwise dictionary. Improved NL self-similarity and local kernel constraint regularization terms are introduced to the image optimization process. Using this, the photometric, geometric, and feature information of the given LR image can be taken into consideration to improve the quality of reconstruction. Simulation results show that the proposed algorithm can achieve better visual effects and the average peak signal-to-noise ratio (PSNR) is improved by approximately 0.5 db compared with the state-of-the-art image SR methods.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Pansharpening Based on Low-Rank and Sparse Decomposition
    • Authors: Rong; K.;Jiao, L.;Wang, S.;Liu, F.;
      Pages: 4793 - 4805
      Abstract: This paper explores the low-rank and sparse (LRS) decomposition to solve the problem of pansharpening. By exploiting the significant correlation among the multispectral (MS) image bands, the LRS decomposition is employed as a decorrelation tool, from which the spectral and spatial informations in MS images can be separated. Based on Go Decomposition (GoDec), we provide two contributions. 1) An LRS-based pansharpening method (i.e., ImPCA) which is designed in terms of component substitution (CS) concept is given. 2) In order to improve the performance of ImPCA by reducing the spectral distortion which is characterized by the color or radiometric changes in the pansharpened images, the local dissimilarity between MS and panchromatic (PAN) images is taken into account by exploiting the context-based decision (CBD) model. Experimental results with both simulated and real data demonstrate that after the local dissimilarity is considered, the quality of the pansharpened images is significantly improved. The improved version of ImPCA is comparable with other popular methods.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Object-Based Image Analysis and Digital Terrain Analysis for Locating
           Landslides in the Urmia Lake Basin, Iran
    • Authors: Blaschke; T.;Feizizadeh, B.;Holbling, D.;
      Pages: 4806 - 4817
      Abstract: The main objective of this research was to establish a semiautomated object-based image analysis (OBIA) methodology for locating landslides. We have detected and delineated landslides within a study area in north-western Iran using normalized difference vegetation index (NDVI), brightness, and textural features derived from satellite imagery (IRS-ID and SPOT-5) in combination with slope and flow direction derivatives from a digital elevation model (DEM) and topographically oriented gray-level cooccurrence matrices (GLCMs). We utilized particular combinations of these information layers to generate objects by applying multiresolution segmentation in a sequence of feature selection and object classification steps. The results were validated by using a landslide inventory database including 109 landslide events. In this study, a combination of these parameters led to a high accuracy of landslide delineation yielding an overall accuracy of 93.07%. Our results confirm the potential of OBIA for accurate delineation of landslides from satellite imagery and, in particular, the ability of OBIA to incorporate heterogeneous parameters such as DEM derivatives and surface texture measures directly in a classification process. The study contributes to the establishment of geographic object-based image analysis (GEOBIA) as a paradigm in remote sensing and geographic information science.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment
           Using Artificial Neural Networks and Logistic Regression Modeling
    • Authors: Elkadiri; R.;Sultan, M.;Youssef, A.M.;Elbayoumi, T.;Chase, R.;Bulkhi, A.B.;Al-Katheeri, M.M.;
      Pages: 4818 - 4835
      Abstract: Efforts to map the distribution of debris flows, to assess the factors controlling their development, and to identify the areas susceptible to their occurrences are often hampered by the paucity of monitoring systems and historical databases in many parts of the world. In this paper, we develop and successfully apply methodologies that rely heavily on readily available remote-sensing datasets over the Jazan province in the Red Sea hills of Saudi Arabia. A fivefold exercise was conducted: 1) a geographical information system (GIS) with a Web interface was generated to host and analyze relevant coregistered remote-sensing data and derived products; 2) an inventory was compiled for debris flows identified from satellite datasets (e.g., GeoEye, Orbview), a subset of which was field verified; 3) spatial analyses were conducted in a GIS environment and 10 predisposing factors were identified; 4) an artificial neural network (ANN) model and a logistic regression (LR) model were constructed, optimized, and validated; and 5) the generated models were used to produce debris-flow susceptibility maps. Findings include: 1) excellent prediction performance for both models (ANN: 96.1%; LR: 96.3%); 2) the high correspondence between model outputs (91.5% of the predictions were common) reinforces the validity of the debris-flow susceptibility results; 3) the variables with the highest predictive power were topographic position index (TPI), slope, distance to drainage line (DTDL), and normalized difference vegetation index (NDVI); and 4) the adopted methodologies are reliable, cost-effective, and could potentially be applied over many of the world’s data-scarce mountainous lands, particularly along the Red Sea Hills.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Crevasse Detection in Ice Sheets Using Ground Penetrating Radar and
           Machine Learning
    • Authors: Williams; R.M.;Ray, L.E.;Lever, J.H.;Burzynski, A.M.;
      Pages: 4836 - 4848
      Abstract: This paper presents methods to automatically classify ground penetrating radar (GPR) images of crevasses on ice sheets. We use a combination of support vector machines (SVMs) and hidden Markov models (HMMs) with down sampling, a preprocessing step that is unbiased and suitable for real-time analysis and detection. We perform modified cross-validation experiments with 129 examples of Greenland GPR imagery from 2012, collected by a lightweight robot towing a GPR. In order to minimize false positives, an HMM classifier is trained to prescreen the data and mark locations in the GPR files to evaluate with an SVM, and we evaluate the classification results with a similar modified cross-validation technique. The combined HMM–SVM method retains all of the correct classifications by the SVM, and reduces the false positive rate to 0.0007. This method also reduces the computational burden in classifying GPR traces because the SVM is evaluated only on select prescreened traces. Our experiments demonstrate the promise, robustness, and reliability of real-time crevasse detection and classification with robotic GPR surveys.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Simulated Annealing for Sequential Pattern Detection and Seismic
    • Authors: Huang; K.;Huang, K.;Chen, I.;Wang, L.K.;
      Pages: 4849 - 4859
      Abstract: Sequential pattern detection with simulated annealing (SA) is adopted to estimate parameters and detect lines, ellipses, hyperbolas type by type, and patterns by patterns in each type. The motivation of the sequential detection method is to deal with multiple patterns. The parameters of a pattern are formed as a vector and used as a state, and adjusted in SA. A sequential detection algorithm using SA to detect patterns is proposed. It detects one or a small number of patterns at each step. SA has the capability of the global minimization. The six parameters of patterns are adjusted sequentially step by step. The computation can converge efficiently. In the experiment, the result of sequential detection is better than that of synchronous detection in detecting a large number of patterns. In sequential detection, detection of one pattern at each step can have less computation time and good convergence in total detection than using two or more pattern detections. In simulated seismic data, SA is applied to detect the hyperbolas in the common depth point (CDP) gather. In real one-shot seismogram, SA is applied to detect lines of direct wave and hyperbolas of reflection wave. The results can show that the proposed method is feasible.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Autofocusing Techniques for GPR Data from RC Bridge Decks
    • Authors: Wei; X.;Zhang, Y.;
      Pages: 4860 - 4868
      Abstract: According to the Federal Highway Administration, more than a quarter of the bridge decks in the United States are obsolete or deficient and most of them are made of reinforced concrete (RC). Precise decisions for bridge maintenance are in high demand due to the limited budget. Among various nondestructive evaluation techniques, ground penetrating radar (GPR) has gained its popularity for reasons such as high speed and fine resolution. Migration is an important intermediate process for GPR data analysis and its accuracy depends on the velocity or dielectric permittivity estimation of the subsurface. Velocity analysis for commonly adopted bistatic GPR systems is based on an iterative trial-and-error process, which involves a human decision process to obtain the properly migrated data. Besides, for heavily polluted data, it can be hardly possible to perform the visual inspection. Autofocusing techniques are rarely discussed in the field of GPR. In this paper, potential autofocusing metrics are nominated and evaluated by both simulation and experimental data. The effects of noise, aperture width, and crossing rebar signals on the metric performance are investigated and the results demonstrate that the higher-order metrics are the most robust and sensitive autofocusing metrics for the migration of GPR data from RC bridge decks.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A Comparative Study of GPR Reconstruction Approaches for Landmine
    • Authors: Gonzalez-Huici; M.A.;Catapano, I.;Soldovieri, F.;
      Pages: 4869 - 4878
      Abstract: This paper compares the performance of three reconstruction techniques frequently applied to process ground penetrating radar (GPR) data in the specific context of landmine detection: 1) Stolt migration, 2) backprojection (BP), and 3) microwave tomographic inversion (MWT). The detection results provided by these algorithms are contrasted with the ones obtained from typically adopted GPR data filtering procedures. To carry out the analysis, we use experimental data collected at a specifically prepared test field, where different targets were buried at shallow depths in inhomogeneous soil. The efficiency of the investigated approaches is quantitatively evaluated in terms of detection accuracies (ROC curves) obtained applying a single pixel-based and an averaged energy detection algorithm. Based on this analysis, we found that the MWT outperforms the other reconstruction algorithms for the smallest mines, which are the most difficult to detect. On the other hand, MWT and BP reconstruction techniques achieve comparable performances against medium mines, and do not improve the outcome in case of big mines with respect to the filtering approaches. Finally, Stolt migration produces the worst results for both medium and small mines.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Synergetic Use of MODIS Cloud Parameters for Distinguishing High Aerosol
           Loadings From Clouds Over the North China Plain
    • Authors: Shang; H.;Chen, L.;Tao, J.;Su, L.;Jia, S.;
      Pages: 4879 - 4886
      Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) standard cloud product is prone to misidentifying areas that are heavily polluted with aerosols as cloudy regions over the North China Plain (NCP) and to retrieving aerosol characteristics as cloud parameters. Based on the differences in physical and optical properties between aerosols and clouds, we propose a new approach to distinguish aerosol-laden areas from cloudy regions using MODIS level 2 cloud properties (e.g., cloud fraction, cloud phase, and cloud top pressure products). The approach was applied to 22 haze-fog cases that occurred in the 2011 and 2012 winters over the NCP. The aerosol identification results were then compared with MODIS-flagged aerosol areas, which were inferred from the noncloud obstruction flag and the suspended dust flag in the MODIS cloud mask product. The results indicated that approximately 60% of the MODIS-flagged aerosol areas were correctly identified using our approach. Among the analyzed cases, two cases exhibited substantial differences; the aerosol areas detected using the newly proposed method were approximately 2.5 times larger than that of the MODIS-flagged area. Further comparisons with aerosol distributions along the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) orbit for the two cases demonstrated that approximately 60%–80% of the CALIOP observed aerosols were identified using our method, while less than 10% of the CALIOP observed aerosols were consistent with the MODIS flagging.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Improved Sub-Pixel Mapping Method Coupling Spatial Dependence With
           Directivity and Connectivity
    • Authors: Ai; B.;Liu, X.;Hu, G.;Li, X.;
      Pages: 4887 - 4896
      Abstract: Accurate land cover mapping by using coarse resolution imageries has been an attractive research topic. Sub-pixel mapping has been proven efficient for allocating sub-pixels within a mixed pixel. The most likely distribution can be determined on the condition of maximized spatial dependence. However, linear land cover like roads and rivers cannot be predicted efficiently because of weaker spatial dependence between and within mixed pixels. To obtain more accurate classification at the sub-pixel scale, an improved sub-pixel mapping method by combining spatial dependence with directivity and connectivity of linear land cover was proposed. Central line of linear land cover was extracted from fraction images to provide site-specific information. Discriminated allocation targets were accordingly designed: both connectivity and directivity were considered as important auxiliary information for allocating linear land cover, whereas only maximized spatial dependence is required for other classes. Then, simulated annealing arithmetic (SAA) was applied to optimize sub-pixel allocation. The method was evaluated visually and quantitatively with the accuracy indices. Compared with the model that considers only spatial dependence, SPM HIIPD method, attraction model and hard classifier (MLC), the improved method can increase classification accuracy at the sub-pixel scale with both simulated imageries and partial SPOT remotely sensed imagery.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A Union Matching Method for SAR Images Based on SIFT and Edge Strength
    • Authors: Chen; T.;Chen, L.;
      Pages: 4897 - 4906
      Abstract: Multiplicative speckle noise often significantly affects the accuracy and adaptability of the scale-invariant feature transform (SIFT) matching method for synthetic aperture radar (SAR) images. To address this problem, this study proposes a union matching method based on the SIFT and edge strength of the SAR image. First, the rotation constraint iteratively refines the initial SIFT match set based on the parameter decomposition of the common geometry transformation model. Using this model, square summation strength (SSS) similarity is then determined. To get the optimal SIFT matches in the searching space, SSS matching based on pixel migration is applied. Furthermore, we outlined the optimization procedure of a union matching strategy. In the iterative process of global searching, the correctly matched tie-points are added individually. Finally, the accuracy, adaptability, and precision of the proposed method are validated through matching experiments on SAR images. Results showed that the proposed method is accurate and robust with respect to the automatic matching of SAR images.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Validating a Notch Filter for Detection of Targets at Sea With ALOS-PALSAR
           Data: Tokyo Bay
    • Authors: Marino; A.;Sugimoto, M.;Ouchi, K.;Hajnsek, I.;
      Pages: 4907 - 4918
      Abstract: The surveillance of maritime areas is a major topic for security aimed at fighting issues as illegal trafficking, illegal fishing, piracy, etc. In this context, Synthetic Aperture Radar (SAR) has proven to be particularly beneficial due to its all-weather and night time acquisition capabilities. Moreover, the recent generation of satellites can provide high quality images with high resolution and polarimetric capabilities. This paper is devoted to the validation of a recently developed ship detector, the Geometrical Perturbations Polarimetric Notch Filter (GP-PNF) exploiting L-band polarimetric data. The algorithm is able to isolate the return coming from the sea background and trigger a detection if a target with different polarimetric behavior is present. Moreover, the algorithm is adaptive and is able to account for changes of sea clutter both in polarimetry and intensity. In this work, the GP-PNF is tested and validated for the first time ever with L-band data, exploiting one ALOS-PALSAR quad-pol dataset acquired on the 9th of October 2008 in Tokyo Bay. One of the motivations of the analysis is also the attempt of testing the suitability of GP-PNF to be used with the new generations of L-band satellites (e.g., ALOS-2). The acquisitions are accompanied by a ground truth performed with a video survey. A comparison with two other detectors is presented, one exploiting a single polarimetric channel and the other considering quad-polarimetric data. Moreover, a test exploiting dual-polarimetric modes (HH/VV and HH/HV) is performed. The GP-PNF shows the capability to detect targets presenting pixel intensity smaller than the surrounding sea clutter in some polarimetric channels. Finally, the quad-polarimetric GP-PNF outperformed in some situations the other two detectors.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Estimation of High-Resolution Land Surface Shortwave Albedo From AVIRIS
    • Authors: He; T.;Liang, S.;Wang, D.;Shi, Q.;Tao, X.;
      Pages: 4919 - 4928
      Abstract: Hyperspectral remote sensing data offer unique opportunities for the characterization of the land surface and atmosphere in the spectral domain. However, few studies have been conducted to estimate albedo from such hyperspectral data. In this study, we propose a novel approach to estimate surface shortwave albedo from data provided by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). Our proposed method is based on the empirical relationship between apparent directional reflectance and surface shortwave broadband albedo established by extensive radiative transfer simulations. We considered the use of two algorithms to reduce data redundancy in the establishment of the empirical relationship including stepwise regression and principle component analysis (PCA). Results showed that these two algorithms were able to produce albedos with similar accuracies. Analysis was carried out to evaluate the effects of surface anisotropy on the direct estimation of broadband albedo. We found that the Lambertian assumption we made in this study did not lead to significant errors in the estimation of broadband albedo from simulated AVIRIS data over snow-free surfaces. Cloud detection was carried out on the AVIRIS images using a Gaussian distribution matching method. Preliminary evaluation of the proposed method was made using AmeriFlux ground measurements and Landsat data, showing that our albedo estimation can satisfy the accuracy requirements for climate and agricultural studies, with respective root-mean-square-errors (RMSEs) of 0.027, when compared with AmeriFlux, and 0.032, when compared with Landsat. Further efforts will focus on the extension and refinement of our algorithm for application to satellite hyperspectral data.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A Robust Nonlocal Fuzzy Clustering Algorithm With Between-Cluster
           Separation Measure for SAR Image Segmentation
    • Authors: Ji; J.;Wang, K.-L.;
      Pages: 4929 - 4936
      Abstract: Fuzzy c-means (FCM) algorithm has been widely used in image segmentation, and there have been many improved algorithms proposed. But when dealing with synthetic aperture radar (SAR) images, they may not give satisfactory segmentation results because of speckle noise. In order to segment SAR image effectively, a robust Fuzzy clustering algorithm is proposed, called nonlocal fuzzy clustering algorithm with between-cluster separation measure (NS_FCM). In NS_FCM, to reduce the effects of the noise, we incorporate the nonlocal spatial information obtained using an improved nonlocal mean method, which adopts adaptive binary weighted distance measure and adaptive filtering degree parameter. In addition, we introduce a fuzzy between-cluster variation term into the objective function. Based on this, while minimizing the objective function, we can maximize the within-cluster compactness measure and the between-cluster separation measure of the partition simultaneously. Besides, by regulating the parameter of the fuzzy between-cluster variation term, we can adjust the distance between the clustering centers flexibly. This makes NS_FCM more effective to the images, which have some close classes in feature space. Experiments on synthetic and real SAR images show that the proposed method behaves well in SAR image segmentation performance.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Micro-Doppler Parameter Estimation via Parametric Sparse Representation
           and Pruned Orthogonal Matching Pursuit
    • Authors: Li; G.;Varshney, P.K.;
      Pages: 4937 - 4948
      Abstract: The rotation, vibration, or coning motion of a target may produce periodic Doppler modulation, which is called the micro-Doppler phenomenon and is widely used for target classification and recognition. In this paper, the signal of interest is decomposed into a family of parametric basis-signals that are generated by discretizing the micro-Doppler parameter domain and synthesizing the micro-Doppler components with over-complete time–frequency characteristics. In this manner, micro-Doppler parameter estimation is converted into the problem of sparse signal recovery with a parametric dictionary. This problem can be considered as a specific case of dictionary learning, i.e., we need to solve for both the sparse solution and the parameter inside the dictionary matrix. To solve this problem, a novel pruned orthogonal matching pursuit (POMP) algorithm is proposed, in which the pruning operation is embedded into the iterative process of the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the proposed approach is validated by simulations.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A Novel Hybrid Method for Remote Sensing Image Approximation Using the
           Tetrolet Transform
    • Authors: Shi; C.;Zhang, J.;Chen, H.;Zhang, Y.;
      Pages: 4949 - 4959
      Abstract: Most existing image sparse approximation methods can reach their best performance only under the condition that the image has some certain properties. In addition, for the remote sensing image, it is difficult to obtain a good sparse result if it contains a lot of details. Focused on the two problems, in this paper, a novel hybrid method that is of some generality is proposed. The method exploits the advantages of the tensor product wavelet transform for representation of smooth images and the ability of the tetrolet transform to represent texture and edge effectively at the same time. Moreover, two specialized processes of decomposition are designed, which contribute to increasing the energy concentration further and preserving the information of the details as much as possible. The procedure of the proposed hybrid method is as follows: for a given remote sensing image, first, the usual tensor product wavelet transform is used, then the redundancy among adjacent wavelet coefficients is removed by making a polyphase decomposition to each subband with a p-fold filter, and after that, the approximation of the low frequency image can be obtained by reconstructing those preserved coefficients. Second, for the detailed image, the sparse decomposition is carried out by the tetrolet transform. For the high frequency subbands, an adaptive decomposition will be done for increasing the energy aggregation. After that, the approximation of the detailed image can be obtained by reconstructing those preserved coefficients. Numerical results indicate the high effectiveness of the procedure for remote sensing image sparse approximation.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Monitoring Tropical Forest Degradation in Betampona Nature Reserve,
           Madagascar Using Multisource Remote Sensing Data Fusion
    • Authors: Ghulam; A.;
      Pages: 4960 - 4971
      Abstract: This paper demonstrates how animal and plant species diversity in the Betampona Nature Reserve (BNR), Madagascar has become threatened through forest degradation and the introduction of invasive species over the last two decades. First, land-use changes and agricultural activities were analyzed using Landsat and IKONOS-2 data from 1990 to 2010. Then, a decision tree algorithm was developed to map under canopy invasive plant species using high resolution optical stereo imaging, land-use classification, and characterizing plant growth using Interferometric Synthetic Aperture Radar (InSAR) and polarimetric InSAR observations from Phased Array type L-band Synthetic Aperture Radar (PALSAR). Next, causal association between land use, climate change, and spatial and temporal dynamics of invasive plant species distribution was explored using satellite derived and in situ climate variables, changes in drought regimes, and tropical cyclones. Results showed that the region experienced intense land-use changes characterized by significant increase in agricultural lands at the cost of primary forest and other land-cover types. Encroachment by habitat-altering invasive plants from 2005 to 2012 within the reserve was obvious, and were probably attributable to illegal logging, erosion of the reserve boundary from anthropogenic activities and cyclone damage as well as shifts in drought regimes. The spatial extent of guava (Psidium cattleianum) has increased from 5.6% of the reserve in 2005 to 7.9% in 2012, a 55-ha increase over less than 7 years. Madagascar cardamom (Aframomum angustifolium) has increased by 1.7% and Molucca raspberry (Rubus moluccanus) by 2.3%, respectively.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • A Method to Differentiate Degree of Volcanic Reservoir Fracture
           Development Using Conventional Well Logging Data—An Application of
           Kernel Principal Component Analysis (KPCA) and Multifractal Detrended
           Fluctuation Analysis (MFDFA)
    • Authors: Ge; X.;Fan, Y.;Zhu, X.;Deng, S.;Wang, Y.;
      Pages: 4972 - 4978
      Abstract: Fracture is the main pore space for volcanic reservoir, serving as the controlling factor of reservoir productivity. Conventional well logging data often fail to fracture characterization and classification in volcanic reservoir since the degree or extent of the fracture development varies in scales in different locations. A method for fracture developing degree discrimination, based on a combinational algorithm of kernel principal component analysis (KPCA) and multifractal detrended fluctuation analysis (KPCA-MFDFA), is proposed. The first kernel principal component ( ${bf KPC_1}$ ), mostly characterizing the reservoir property, is extracted from conventional well logging data. Multifractal parameters, such as multifractal dimension, mass exponent, multifractal spectrum, and singularity strength, are calculated by MFDFA. A cross-plot between the maximum multifractal dimension difference and range of singularity strength is established to investigate the relationships between multifractal parameters and fracture developing degree.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • Open Access
    • Pages: 4979 - 4979
      Abstract: Advertisement: This publication offers open access options for authors. IEEE open access publishing.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • IEEE xplore digital library
    • Pages: 4980 - 4980
      Abstract: Advertisement: IEEE Xplore digital library. Driving research at the world's leading universities and institutions.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
  • 2014 Index IEEE Journal of Selected Topics in Applied Earth Observations
           and Remote Sensing Vol. 7
    • Pages: 4981 - 5049
      Abstract: This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.
      PubDate: Dec. 2014
      Issue No: Vol. 7, No. 12 (2014)
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