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  Subjects -> ELECTRONICS (Total: 138 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 4)
Advances in Microelectronic Engineering     Open Access   (Followers: 1)
Advances in Power Electronics     Open Access   (Followers: 7)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 50)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Annals of Telecommunications     Hybrid Journal   (Followers: 4)
APL : Organic Electronics and Photonics     Hybrid Journal   (Followers: 1)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 2)
Archives of Electrical Engineering     Open Access   (Followers: 8)
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: 14)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 11)
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: 12)
China Communications     Full-text available via subscription   (Followers: 1)
Circuits and Systems     Open Access   (Followers: 7)
Consumer Electronics Times     Open Access   (Followers: 3)
Control Systems     Hybrid Journal   (Followers: 18)
Electronic Markets     Hybrid Journal   (Followers: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 2)
Electronics     Open Access   (Followers: 3)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 4)
Electronics Letters     Hybrid Journal   (Followers: 15)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 14)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 8)
Foundations and TrendsĀ® in Communications and Information Theory     Full-text available via subscription   (Followers: 5)
Foundations and TrendsĀ® in Signal Processing     Full-text available via subscription   (Followers: 4)
Frequenz     Full-text available via subscription   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 19)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 7)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 3)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription  
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 9)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 19)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 10)
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: 11)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 7)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Power Electronics     Hybrid Journal   (Followers: 7)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 5)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 4)
IETE Technical Review     Open Access   (Followers: 1)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 9)
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: 3)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 1)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 10)
International Journal of Applied Electronics in Physics & Robotics     Open Access  
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (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: 2)
International Journal of Control     Hybrid Journal   (Followers: 9)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 3)
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: 1)
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: 3)
International Journal of Power Management Electronics     Open Access  
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 1)
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: 7)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 11)
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: 1)
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: 2)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 1)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 1)
Journal of Electronics (China)     Hybrid Journal   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 49)
Journal of Intelligent Procedures in Electrical Technology     Open Access  
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 4)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 1)

        1 2 | Last

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
   [17 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  [172 journals]   [SJR: 1.232]   [H-I: 14]
  • 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: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Pages: C4 - C4
      Abstract: The IEEE Geoscience and Remote Sensing Society is grateful for the support given by the organizations listed below and invites applications for Institutional Listings from other firms interested in the field of Geoscience and Remote Sensing.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • [Front cover]
    • Pages: C1 - C1
      Abstract: Presents the front cover for this issue of the publication.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (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: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Table of contents
    • Pages: 1005 - 1006
      Abstract: Presents the table of contents for this issue of the periodical.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Foreword to the Special Issue on Machine Learning for Remote Sensing Data
    • Authors: Tuia; D.;Merenyi, E.;Jia, X.;Grana-Romay, M.;
      Pages: 1007 - 1011
      Abstract: The twenty-seven articles in this special issue is a follow-up to special sessions organized at WHISPERS conferences. Such sessions drew unexpectedly large attendance, signaling the interest and need for a focused platform to exchange knowledge at the intersection of machine learning and remote sensing. The collection of papers in this special issue presents a comprehensive sample of the latest trends in the design of machine learning algorithms for geospatial data. It covers a wide spectrum of remote sensing applications and presents new solutions to answer to the call of the new generation of sensors, covering the electromagnetic range from optical to microwave data.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image
    • Authors: Li; W.;Du, Q.;
      Pages: 1012 - 1022
      Abstract: By coupling the nearest-subspace classification with a distance-weighted Tikhonov regularization, nearest regularized subspace (NRS) was recently developed for hyperspectral image classification. However, the NRS was originally designed to be a pixel-wise classifier which considers the spectral signature only while ignoring the spatial information at neighboring locations. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. In this paper, we mainly exploit the benefits of using spatial features extracted from a simple Gabor filter for the NRS classifier. The proposed Gabor-filtering-based classifier has been validated on several real hyperspectral datasets. Experimental results demonstrate that the proposed method significantly increases the classification accuracy compared to conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine and sparse-representation-based classification.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • A Two-Stage Feature Selection Framework for Hyperspectral Image
           Classification Using Few Labeled Samples
    • Authors: Jia; S.;Zhu, Z.;Shen, L.;Li, Q.;
      Pages: 1023 - 1035
      Abstract: Although the high dimensionality of hyperspectral data increases the separability of land covers, it is difficult to distinguish certain classes using only the spectral information due to the widespread mixed pixels and small sample size problems. Three-dimensional Gabor wavelet transform takes the entire hyperspectral data cube as a tensor, captures the joint spectral-spatial structures very well and has shown great potential to improve classification accuracies. However, much redundancy exists in the extracted huge amount of Gabor features, which inevitably degrades the efficiency of the method. To make matters worse, according to the Hughes phenomenon, the less informative bands/features may sacrifice the classification accuracy. In this paper, a two-stage feature selection framework, Affinity Propagation-Gabor-Conditional Mutual Information (abbreviated as AP-Gabor-CMI), is proposed to deal with the problems, which chooses the most important features before and after the Gabor wavelet-based feature extraction procedure. Specifically, the first stage picks out the most distinctive bands from the original hyperspectral data through complex wavelet structural similarity (CW-SSIM) index based affinity propagation clustering algorithm. After applying the Gabor wavelet-based feature extraction on the chosen bands, the second stage selects the most discriminative features from them by means of conditional mutual information-based feature ranking and elimination. Experimental results on three real hyperspectral data sets demonstrate the advantages of the proposed two-stage feature selection framework and the superiority of AP-Gabor-CMI over state-of-the-art methods when only few labeled samples per class are available.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • A Batch-Mode Active Learning Algorithm Using Region-Partitioning Diversity
           for SVM Classifier
    • Authors: Huo; L.-Z.;Tang, P.;
      Pages: 1036 - 1046
      Abstract: In this paper, a region-partitioning active learning (AL) technique is proposed for classification of remote sensing (RS) images based on the support vector machines (SVM) classifier. In the batch-mode AL process, diversity information is required to select a batch of informative samples. A new AL technique that aims to introduce diversity information is proposed based on relative positions of candidate samples in the feature space. The proposed technique selects informative samples according to an uncertainty criterion at each iteration. These samples are selected with an extra constraint to guarantee that they are not located in the same region of the feature space. The proposed technique is compared with state-of-the-art methods adopted in the RS community. Experimental tests were performed on three data sets, including one very high spatial resolution multispectral data set and two hyperspectral data sets. The proposed algorithm displays a classification performance that is similar to or even better than the state-of-the-art methods. In addition, the proposed algorithm performs efficiently in terms of computational time.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Spectral–Spatial Preprocessing Using Multihypothesis Prediction for
           Noise-Robust Hyperspectral Image Classification
    • Authors: Chen; C.;Li, W.;Tramel, E.W.;Cui, M.;Prasad, S.;Fowler, J.E.;
      Pages: 1047 - 1059
      Abstract: Spectral–spatial preprocessing using multihypothesis prediction is proposed for improving accuracy of hyperspectral image classification. Specifically, multiple spatially collocated pixel vectors are used as a hypothesis set from which a prediction for each pixel vector of interest is generated. Additionally, a spectral-band-partitioning strategy based on inter-band correlation coefficients is proposed to improve the representational power of the hypothesis set. To calculate an optimal linear combination of the hypothesis predictions, a distance-weighted Tikhonov regularization to an ill-posed least-squares optimization is used. The resulting predictions effectively integrate spectral and spatial information and thus are used during classification in lieu of the original pixel vectors. This processed hyperspectral image dataset has less intraclass variability and more spatial regularity as compared to the original dataset. Classification results for two hyperspectral image datasets demonstrate that the proposed method can enhance the classification accuracy of both maximum-likelihood and support vector classifiers, especially under small sample size constraints and noise corruption.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • +++:+Ensemble+Extreme+Learning+Machines+for+Hyperspectral+Image+Classification&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&;Du,++P.;Liu,++S.;Li,++J.;Cheng,++L.;"> {{\rm<br>       E}^{2}}{\rm LMs} : Ensemble Extreme Learning Machines for
           Hyperspectral Image Classification
    • Authors: Samat; A.;Du, P.;Liu, S.;Li, J.;Cheng, L.;
      Pages: 1060 - 1069
      Abstract: Extreme learning machine (ELM) has attracted attentions in pattern recognition field due to its remarkable advantages such as fast operation, straightforward solution, and strong generalization. However, the performance of ELM for high-dimensional data, such as hyperspectral image, is still an open problem. Therefore, in this paper, we introduce ELM for hyperspectral image classification. Furthermore, in order to overcome the drawbacks of ELM caused by the randomness of input weights and bias, two new algorithms of ensemble extreme learning machines (Bagging-based and AdaBoost-based ELMs) are proposed for the classification task. In order to illustrate the performance of the proposed algorithms, support vector machines (SVMs) are used for evaluation and comparison. Experimental results with real hyperspectral images collected by reflective optics spectrographic image system (ROSIS) and airborne visible/infrared imaging spectrometer (AVIRIS) indicate that the proposed ensemble algorithms produce excellent classification performance in different scenarios with respect to spectral and spectral–spatial feature sets.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • PerTurbo Manifold Learning Algorithm for Weakly Labeled Hyperspectral
           Image Classification
    • Authors: Chapel; L.;Burger, T.;Courty, N.;Lefevre, S.;
      Pages: 1070 - 1078
      Abstract: Hyperspectral data analysis has been given a growing attention due to the scientific challenges it raises and the wide set of applications that can benefit from it. Classification of hyperspectral images has been identified as one of the hottest topics in this context, and has been mainly addressed by discriminative methods such as SVM. In this paper, we argue that generative methods, and especially those based on manifold representation of classes in the hyperspectral space, are relevant alternatives to SVM. To illustrate our point, we focus on the recently published PerTurbo algorithm and benchmark against SVM this generative manifold learning algorithm in the context of hyperspectral image classification. This choice is motivated by the fact that PerTurbo is fitted with numerous interesting properties, such as 1) low sensitivity to dimensionality curse, 2) high accuracy in weakly labelled images classification context (few training samples), 3) straightforward extension to on-line setting, and 4) interpretability for the practitioner. The promising results call for an up-to-date interest toward generative algorithms for hyperspectral image classification.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • An Evaluation of Low-Rank Mahalanobis Metric Learning Techniques for
           Hyperspectral Image Classification
    • Authors: Bue; B.D.;
      Pages: 1079 - 1088
      Abstract: We provide a comparative study of several state-of-the-art Mahalanobis metric learning algorithms evaluated on three well-studied, high-dimensional hyperspectral images captured by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument. We focus on the problem of low-rankMahalanobis metric learning, where our objective is to learn an ${mbi{n}} {mmb times} {mbi{m}}$ projection matrix ${bf A}$ , where ${mbi{m}} {mmb ll} {mbi{n}}$ . Low-rank metrics offer a “plug-in” enhancement to similarity-based classifiers that can reduce computation time and improve classification accuracy with fewer training samples, enabling operations in resource-constrained environments such as onboard spacecraft. Our results indicate that applying a simple shrinkage-based regularization procedure to multiclass Linear Discriminant Analysis (LDA) produces comparable or better classification accuracies than the low-rank extensions of several widely used Mahalanobis metric learning algorithms, at considerably lower computational cost.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Ensemble Learning in Hyperspectral Image Classification: Toward Selecting
           a Favorable Bias-Variance Tradeoff
    • Authors: Merentitis; A.;Debes, C.;Heremans, R.;
      Pages: 1089 - 1102
      Abstract: Automated classification of hyperspectral images is a fast growing field with numerous applications in the areas of security and surveillance, agriculture, urban management, and environmental monitoring. Although significant progress has been achieved in various aspects of hyperspectral classification (e.g., feature extraction, feature selection, classification, and post-classification processing), the problem has not been addressed so far from a bias-variance decomposition point of view. In this work, we introduce a consistent unified framework that jointly considers all steps in the hyperspectral image classification chain from a bias-variance decomposition perspective. Additionally, we show how state-of-the-art techniques in feature extraction, ensemble-based classification, and post-classification segmentation are related to the bias-variance tradeoff and how this relation can be used to improve classification accuracy. An important outcome of our analysis is that all the steps of the classification chain should be optimized jointly as this unified optimization can guide toward a more favorable bias-variance tradeoff. Experimental results of the proposed framework in the case of four hyperspectral datasets prove the effectiveness of our approach.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images
    • Authors: Faria; F.A.;Pedronette, D.C.G.;dos Santos, J.A.;Rocha, A.;Torres, R.d.S.;
      Pages: 1103 - 1115
      Abstract: In the past few years, segmentation and classification techniques have become a cornerstone of many successful remote sensing algorithms aiming at delineating geographic target objects. One common strategy relies on using multiple complex features to guide the delineation process with the objective of gathering complementary information for improving classification results. However, a persistent problem in this approach is how to combine different and noncorrelated feature descriptors automatically. In this regard, one solution is to combine them through multiple classifier systems (MCSs) in which the diversity of simple/noncomplex classifiers is an essential issue in the definition of appropriate strategies for classifier fusion. In this paper, we propose a novel strategy for selecting classifiers (whereby a classifier is taken as a pair of learning method plus image descriptor) to be combined in MCS. In the proposed solution, diversity measures are used to assess the degree of agreement/disagreement between pairs of classifiers and ranked lists are created to sort them according to their diversity score. Thereafter, the classifiers are also sorted according to their performance through different evaluation measures (e.g., kappa and tau indices). In the end, a rank aggregation method is proposed to select the most suitable classifiers based on both the diversity and the effectiveness performance of classifiers. The proposed fusion framework has targeted at coffee crop classification and urban recognition but it is general enough to be used in a variety of other pattern recognition problems. Experimental results demonstrate that the novel strategy yields good results when compared to several baselines while using fewer classifiers and being much more efficient.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • SAR Image Classification Through Information-Theoretic Textural Features,
           MRF Segmentation, and Object-Oriented Learning Vector Quantization
    • Authors: DElia; C.;Ruscino, S.;Abbate, M.;Aiazzi, B.;Baronti, S.;Alparone, L.;
      Pages: 1116 - 1126
      Abstract: Segmentation of optical images may be obtained through algorithms based on image prior models that exploit the spatial dependencies of land covers. In synthetic aperture radar (SAR) images, speckle conceals such spatial dependencies and segmentation algorithms suitable for optical images may become ineffective. Textural features may be used to emphasize spatial dependencies in the data and hence to improve segmentation. Once segmentation has been accomplished, a number of shapes is available. In this paper, the problem is tackled through the joint use of information-theoretic (IT) SAR features, of a segmentation algorithm based on tree structured Markov random fields (TS-MRFs), and of object-oriented classification achieved through learning vector quantization (LVQ). The proposed system works with one or more coregistered images, not necessarily all SAR, and one or more spatial maps of pixel features derived from each input image. A unique partition into connected regions, or segments, is achieved from the plurality of input channels, either images or feature maps. From each segment, representing a shape, geometric, radiometric, and textural parameters are extracted and fed to an LVQ classifier, trained through a partial reference ground truth (GT) of the scene. Classification results on a textured SAR image of a city and its surroundings validate the proposed object-oriented approach. Good performances can be achieved with small sizes of training sets, but they can be improved by using a decision fusion through majority voting (MV) of the outcomes of several experiments.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine
           Cascaded Active Learning
    • Authors: Blanchart; P.;Ferecatu, M.;Cui, S.;Datcu, M.;
      Pages: 1127 - 1141
      Abstract: Pattern retrieval is a fundamental challenge in machine learning but is often subject to the problem of gathering enough labeled examples of the target pattern, and also to the computational complexity inherent to the training and the evaluation of complex classifier functions on large databases. In this paper, we propose a hierarchical top-down processing scheme for pattern retrieval in high-volume high-resolution optical satellite image repositories. We learn via a multistage active learning process a cascade of classifiers working each at a certain scale on a patch-based representation of images. At each stage of the hierarchy, we seek to eliminate large parts of images considered as nonrelevant, the purpose being to set the focus at the finest scales on more promising and as spatially limited as possible areas. Our scheme is based on the fact that by reducing the size of the analysis window (i.e., the size of the patch), we better capture the properties of the targeted object. The cascaded hierarchy is introduced to compensate for the extra computational burden incurred by diminishing the size of the patch, which causes an explosion of the number of patches to process. Unlike most other retrieval methods, which require large training sets and costly offline training, we propose a cascaded active learning strategy to build a classifier at each level of the hierarchy, and we provide a new Multiple Instance Learning algorithm to propagate automatically the training examples from one level of the hierarchy to the other. Two study cases are performed for validation. The first is a test on a database of 61-cm resolution QuickBird panchromatic images and the second is an example of temporal pattern retrieval from a database of Synthetic Aperture Radar (SAR) image time series. These tests show that our method achieves a reduction in the number of computations of two orders of magnitude, while keeping the same accuracy level as recent state-of-the-art methods.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Phenology-Driven Land Cover Classification and Trend Analysis Based on
           Long-term Remote Sensing Image Series
    • Authors: Xue; Z.;Du, P.;Feng, L.;
      Pages: 1142 - 1156
      Abstract: The objective of this study is to classify the land cover types and analyze the land cover trend by incorporating phenological variability throughout a range of natural ecosystems using time-series remotely sensed images. First, a breaks for additive seasonal and trend (BFAST) approach is used to extract the phenology information from the time series. Second, a dynamic time warping (DTW) approach is adopted to screen the additional interpreted samples used for training. Third, some ensemble learning classifiers and the support vector machine (SVM) are performed to classify the land cover types based on the BFAST-derived phenology components. Finally, some inter-annual phenological markers are extracted to facilitate the land cover trend analysis by taking the climate fluctuations and anthropogenic forcing into consideration. The experimental results with normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data collected by the Moderate Resolution Imaging Spectrometer (MODIS) indicate that the classification accuracy is significantly improved by using the phenology information and the phenological markers can lead to a better understanding of the regional land cover change.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • ++++(+++)+AVHRR+Long-Term+Data+Record+(LTDR)+and+Landsat+TM+Archive+to+Map+Large+Fires+in+the+North+American+Boreal+Region+From+1984+to+1998&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&;Garcia-Lazaro,++J.R.;Riano,++D.;Kefauver,++S.C.;">The Synergy of the        src="/images/tex/21451.gif" alt="0.05^\circ"> (       formulatype="inline"> \sim5nbsp\hbox<br>       {km} ) AVHRR Long-Term Data Record (LTDR) and Landsat TM
           Archive to Map Large Fires in the North American Boreal Region From 1984
           to 1998
    • Authors: Moreno-Ruiz; J.A.;Garcia-Lazaro, J.R.;Riano, D.;Kefauver, S.C.;
      Pages: 1157 - 1166
      Abstract: A Bayesian network classifier-based algorithm was applied to map the burned area (BA) in the North American boreal region using the $0.05^circ$ ( $sim5nbsphbox{km}$ ) Advanced Very High Resolution Radiometer (AVHRR) Long-Term Data Record (LTDR) data version 3 time series. The results showed an overall good agreement compared to reference maps ( $hbox{slope} = 0.62; {R^2} = 0.75$ ). The study site was divided into six sub-regions, where south-western Canada performed the worst ( $hbox{slope} = 0.25; {R^2} = 0.47$ ). The algorithm achieved good results as long as a year with high fire incidence was employed to train the Bayesian network, and the vegetation response to fire remained consistent across the region. Years with higher fire activity and larger fires, which were easier to detect at the LTDR spatial scale, matched the reference maps better. The LTDR postfire signal remained detectable for 6–9 years, extending opportunities to map the full fire extent with Landsat Thematic Mapper (TM). For fires larger than $1000nbsphbox{km}^{2}$ , Landsat TM mapped 99%, whereas LTDR caught 69% of the reference BA reported. Landsat TM took four satellite overpasses (2 months) to map these large fires, and in some cases even until the following year, but LTDR detected them within days. Thus, results suggest that LTDR could be used to trigger the search for fires and then map their perimeter with Landsat TM. This study demonstrates an LTDR BA algorithm that could be extrapolated to other boreal regions using a similar methodology, although reference fire perimeters would be needed to train the Bayesian classifier and its thresholds.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • A Generic Land-Cover Classification Framework for Polarimetric SAR Images
           Using the Optimum Touzi Decomposition Parameter Subset—An Insight
           on Mutual Information-Based Feature Selection Techniques
    • Authors: Banerjee; B.;Bhattacharya, A.;Buddhiraju, K.M.;
      Pages: 1167 - 1176
      Abstract: This correspondence proposes a generic framework for land-cover classification using support vector machine (SVM) classifier for polarimetric synthetic aperture radar (SAR) images considering the optimum Touzi decomposition parameters. Some new concerns have been raised recently with the Cloude–Pottier decomposition. Cloude’s $alphab$ scattering type ambiguities may take place for certain scatterers, and some of the Cloude–Pottier’s parameters may not be roll-invariant for asymmetric targets. The Touzi decomposition is a relatively new roll-invariant target scattering decomposition, and it uses the target helicity, symmetric scattering type magnitude and phase. The parameters generated by the Touzi decomposition are of different physical significances, i.e., some of them are angular in nature where others are from ${mbi{BBR}}$ . Thus, classification using the Touzi parameters requires them to be normalized within the similar dynamic range preserving their physical properties. Here, a linear normalization technique has been introduced, which maps the angular parameters to ${mbi{BBR}}$ without loss of generalization. The power of mutual information (MI) has been explored hence after for selecting the optimum set of classification parameters. A third-order class-dependent MI-based method and another method based on the Eigen-space decomposition of the class conditional MI matrix have been introduced for this purpose. For SVM-based final classification, a normalized histogram intersection kernel (NIKSVM) has been proposed that boosts the generalization accuracy to a considerable extent as compared to normal histogram intersection kernel. An ALOS L-band SAR image of Mumbai area, India has been considered here to exhibit the performance o- the proposed cost-effective classification framework.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Spectral–Spatial Classification of Hyperspectral Images Using
           Wavelets and Extended Morphological Profiles
    • Authors: Quesada-Barriuso; P.;Arguello, F.;Heras, D.B.;
      Pages: 1177 - 1185
      Abstract: This paper deals with hyperspectral image classification in remote sensing. The proposed scheme is a spectral–spatial technique based on wavelet transforms and mathematical morphology. The original contribution of this paper is that the extended morphological profile (EMP) is created from the features extracted by wavelets, which has proven to be better or comparable to other techniques for dimensionality reduction of hyperspectral data. In addition, the hyperspectral image is denoised, also using wavelets, with the objective of removing undesirable artifacts introduced in the acquisition of the data. The classification is carried out by a support vector machine (SVM) classifier. The accuracy is improved when comparing with previously developed spectral–spatial SVM-based schemes.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Comparative Assessment of Supervised Classifiers for Land Use–Land
           Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic
    • Authors: Shiraishi; T.;Motohka, T.;Thapa, R.B.;Watanabe, M.;Shimada, M.;
      Pages: 1186 - 1199
      Abstract: Numerous classification algorithms have been proposed to create accurate classification maps using optical remote sensing data. However, few comparative studies evaluate the performance of classification algorithms with focus on tropical forests due to cloud effects. Advances in synthetic aperture radar (SAR) techniques and spatial resolution, mapping, and comparison of classification algorithms are possible. This research investigated the accuracy and processing speeds of five supervised classifiers, including Naïve Bayes, AdaBoost, multi-layer perceptron, random forest (RF), and support vector machine, for land use–land cover (LULC) classification in a tropical region using time-series Advanced Land Observing Satellite-phased array type L-band SAR (ALOS-PALSAR) 25-m mosaic data. The study area is located in central Sumatra, Indonesia, where abundant forest-related carbon stocks exist. This investigation was intended to aid the implementation of a classification algorithm for the automatic creation of LULC classification maps. We perform object-based and pixel-based analyses to investigate the ability of the classifiers and their accuracies, respectively. RF had the best classification accuracy and processing speed in which the accuracies for 10 classes and 2 classes were 64.07% and 90.22% for pixel-based and 82.94% and 86.23% for object-based evaluations, respectively. These results indicate that RF is a useful classifier for the analysis of PALSAR mosaic data and that the automatic creation of highly accurate classification maps is possible by using time-series data. The outcome of this research will be valuable resources for biodiversity and global-warming mitigation efforts in the region.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • A Neural Approach Under Active Learning Mode for Change Detection in
           Remotely Sensed Images
    • Authors: Roy; M.;Ghosh, S.;Ghosh, A.;
      Pages: 1200 - 1206
      Abstract: In this paper, a change detection technique using neural networks in active learning framework is proposed under the scarcity of labeled patterns. In the present investigation, two variants of radial basis function neural networks and a multilayer perceptron are used as learners. Instead of training the network (or ensemble of networks) with randomly collected labeled patterns, in the proposed work, the network (or ensemble of networks) is iteratively trained with label patterns, collected using the query functions. Here, two query selection strategies are used: uncertainty sampling and query-by-committee. In this way, the most informative set of labeled patterns can be iteratively generated by querying. To evaluate the effectiveness of the proposed approach, the experiments are conducted on multi-temporal remotely sensed images. The results obtained using the proposed active learning framework are found to be encouraging.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Extraction of Built-up Areas From Fully Polarimetric SAR Imagery Via PU
    • Authors: YANG; W.;Yin, X.;Song, H.;Liu, Y.;Xu, X.;
      Pages: 1207 - 1216
      Abstract: In this paper, we propose a PU learning (i.e., learning from positive and unlabeled data, which trains a binary classifier using only PU examples) based method for extracting the built-up areas (BAs) from fully polarimetric synthetic aperture radar (PolSAR) imagery. The key feature is that there are no labeled negative training data, thus the traditional classification techniques are not applicable. To solve this problem, we use a two-step strategy-based PU learning. In the first step, an improved algorithm yields reliable negative samples from an unlabeled set. In the second step, we apply a support vector machine iteratively to these negative samples, existing positive samples and the remaining unlabeled samples. Finally, we select a classifier after convergence. To make the method suitable for BA extraction from PolSAR imagery, an extended scattering mechanism-based statistical feature using the adaptive model decomposition is introduced as the feature descriptor. Experimental results for RADARSAT-2 PolSAR data sets demonstrate the effectiveness of our method, which achieves satisfactory accuracy with less manual labeling.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Optimal Sparse Kernel Learning in the Empirical Kernel Feature Space for
           Hyperspectral Classification
    • Authors: Gurram; P.;Kwon, H.;
      Pages: 1217 - 1226
      Abstract: In this paper, a novel framework for optimal sparse kernel learning for support vector machine (SVM) classifier in a finite-dimensional space called the empirical kernel feature space (EKFS) is presented. In conventional sparse kernel learning techniques, feature selection algorithms are optimal up to linear kernel because the contributions of individual features in the input space to the margin of the classifier can be determined explicitly for a linear kernel. But the use of nonlinear kernels leads to high dimensional, possibly infinite dimensional, Reproducing Kernel Hilbert Spaces (RKHS). Here, feature selection problem is highly combinatorial, and is NP-hard to solve because the number of all the possible combinations of the input features mapped from the input space into the RKHS is often prohibitively large to determine the contributions of subsets of features. To tackle this issue, in the proposed work, feature selection is explicitly and optimally performed in the EKFS instead of in the corresponding RKHS. Unlike the RKHS, the EKFS associated with any positive definite kernel including Gaussian RBF kernel can explicitly be built by using empirical kernel mapping. The feature selection in the EKFS has the same effect as the feature selection in the RKHS since both the EKFS and RKHS associated with same kernel have the same geometrical structure. The features in the EKFS are a kernel representation of each input vector with respect to all the training samples available. Thus, they represent nonlinear similarity measure of each data point with respect to reference samples with known labels. The proposed sparse kernel learning can optimally select multiple subsets of newly mapped features in the EKFS in order to improve the generalization performance of the classifier. The sparse kernel-based learning is tested on several hyperspectral datasets and a performance comparison among different feature selection techniques is presented.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • A Robust Nonlinear Hyperspectral Anomaly Detection Approach
    • Authors: Zhao; R.;Du, B.;Zhang, L.;
      Pages: 1227 - 1234
      Abstract: This paper proposes a nonlinear version of an anomaly detector with a robust regression detection strategy for hyperspectral imagery. In the traditional Mahalanobis distance-based hyperspectral anomaly detectors, the background statistics are easily contaminated by anomaly targets, resulting in a poor detection performance. The traditional detectors also often fail to detect anomaly targets when the samples in the image do not conform to a Gaussian normal distribution. In order to solve these problems, this paper proposes a robust nonlinear anomaly detection (RNAD) method by utilizing robust regression analysis in the kernel feature space. Using the robust regression detection strategy, this method can suppress the contamination of the detection statistics by anomaly targets. Moreover, in this anomaly detection method, the input data are implicitly mapped into an appropriate high-dimensional kernel feature space by nonlinear mapping, which is associated with the selected kernel function. Experiments were conducted on synthetic data and an airborne AVIRIS hyperspectral image, and the experimental results indicate that the proposed hyperspectral anomaly detection approach in this paper outperforms three state-of-art commonly used anomaly detection algorithms.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • An Adaptive Memetic Fuzzy Clustering Algorithm With Spatial Information
           for Remote Sensing Imagery
    • Authors: Zhong; Y.;Ma, A.;Zhang, L.;
      Pages: 1235 - 1248
      Abstract: Due to its inherent complexity, remote sensing image clustering is a challenging task. Recently, some spatial-based clustering approaches have been proposed; however, one crucial factor with regard to their clustering quality is that there is usually one parameter that controls their spatial information weight, which is difficult to determine. Meanwhile, the traditional optimization methods of the objective functions for these clustering approaches often cannot function well because they cannot simultaneously possess both a local search capability and a global search capability. Furthermore, these methods only use a single optimization method rather than hybridizing and combining the existing algorithmic structures. In this paper, an adaptive fuzzy clustering algorithm with spatial information for remote sensing imagery (AFCM_S1) is proposed, which defines a new objective function with an adaptive spatial information weight by using the concept of entropy. In order to further enhance the capability of the optimization, an adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery (AMASFC) is also proposed. In AMASFC, the clustering problem is transformed into an optimization problem. A memetic algorithm is then utilized to optimize the proposed objective function, combining the global search ability of a differential evolution algorithm with a local search method using Gaussian local search (GLS). The optimal value of the specific parameter in GLS, which determines the local search efficiency, can be obtained by comparing the objective function increment for different values of the parameter. The experimental results using three remote sensing images show that the two proposed algorithms are effective when compared with the traditional clustering algorithms.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Toward a Semiautomatic Machine Learning Retrieval of Biophysical
    • Authors: Caicedo; J.P.R.;Verrelst, J.;Munoz-Mari, J.;Moreno, J.;Camps-Valls, G.;
      Pages: 1249 - 1259
      Abstract: Biophysical parameters such as leaf chlorophyll content (LCC) and leaf area index (LAI) are standard vegetation products that can be retrieved from Earth observation imagery. This paper introduces a new machine learning regression algorithms (MLRAs) toolbox into the scientific Automated Radiative Transfer Models Operator (ARTMO) software package. ARTMO facilitates retrieval of biophysical parameters from remote observations in a MATLAB graphical user interface (GUI) environment. The MLRA toolbox enables analyzing the predictive power of various MLRAs in a semiautomatic and systematic manner, and applying a selected MLRA to multispectral or hyperspectral imagery for mapping applications. It contains both linear and nonlinear state-of-the-art regression algorithms, in particular linear feature extraction via principal component regression (PCR), partial least squares regression (PLSR), decision trees (DTs), neural networks (NNs), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The performance of multiple implemented regression strategies has been evaluated against the SPARC dataset (Barrax, Spain) and simulated Sentinel-2 (8 bands), CHRIS (62 bands) and HyMap (125 bands) observations. In general, nonlinear regression algorithms (NN, KRR, and GPR) outperformed linear techniques (PCR and PLSR) in terms of accuracy, bias, and robustness. Most robust results along gradients of training/validation partitioning and noise variance were obtained by KRR while GPR delivered most accurate estimations. We applied a GPR model to a hyperspectral HyMap flightline to map LCC and LAI. We exploited the associated uncertainty intervals to gain insight in the per-pixel performance of the model.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • A Neural Network Retrieval Technique for High-Resolution Profiling of
           Cloudy Atmospheres
    • Authors: Blackwell; W.J.;Milstein, A.B.;
      Pages: 1260 - 1270
      Abstract: The synergistic use of microwave and hyperspectral infrared sounding observations gives rise to a rich array of signal processing challenges. Of particular interest are the following elements which are combined for the first time in the retrieval technique presented here: 1) radiance noise filtering and redundancy removal (compression) using principal components transforms and canonical correlations, 2) data fusion (infrared plus microwave at possibly different spatial and spectral resolutions) and stochastic cloud clearing (SCC), and 3) geophysical product retrieval from spectral radiance measurements using neural networks. In this paper, we describe the algorithm and demonstrate performance using the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). We show that performance is improved by approximately 25%–50% using the neural network method relative to other common techniques. Furthermore, we quantify the improvement in the vertical resolution of the retrieved products.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Example-Based Super-Resolution Land Cover Mapping Using Support Vector
    • Authors: Zhang; Y.;Du, Y.;Ling, F.;Fang, S.;Li, X.;
      Pages: 1271 - 1283
      Abstract: Super-resolution mapping (SRM) is a promising technique to generate a fine resolution land cover map from coarse fractional images by predicting the spatial locations of different land cover classes at subpixel scale. In most cases, SRM is accomplished by using the spatial dependence principle, which is a simple method to describe the spatial patterns of different land cover classes. However, the spatial dependence principle used in existing SRM models does not fully reflect the real-world situations, making the resultant fine resolution land cover map often have uncertainty. In this paper, an example-based SRM model using support vector regression (SVR_SRM) was proposed. Without directly using an explicit formulation to describe the prior information about the subpixel spatial pattern, SVR_SRM generates a fine resolution land cover map from coarse fractional images, by learning the nonlinear relationships between the coarse fractional pixels and corresponding labeled subpixels from the selected best-match training data. Based on the experiments of two subset images of National Land Cover Database (NLCD) 2001 and a subset of real hyperspectral Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image, the performance of SVR_SRM was evaluated by comparing with the traditional pixel-based hard classification (HC) and several existing typical SRM algorithms. The results show that SVR_SRM can generate fine resolution land cover maps with more detailed spatial information and higher accuracy at different spatial scales.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • An Online Coupled Dictionary Learning Approach for Remote Sensing Image
    • Authors: Guo; M.;Zhang, H.;Li, J.;Zhang, L.;Shen, H.;
      Pages: 1284 - 1294
      Abstract: Most earth observation satellites, such as IKONOS, QuickBird, GeoEye, and WorldView-2, provide a high spatial resolution (HR) panchromatic (Pan) image and a multispectral (MS) image at a lower spatial resolution (LR). Image fusion is an effective way to acquire the HR MS images that are widely used in various applications. In this paper, we propose an online coupled dictionary learning (OCDL) approach for image fusion, in which a superposition strategy is applied to construct the coupled dictionaries. The constructed coupled dictionaries are further developed via an iterative update to ensure that the HR MS image patch can be almost identically reconstructed by multiplying the HR dictionary and the sparse coefficient vector, which is solved by sparsely representing its counterpart LR MS image patch over the LR dictionary. The fusion results from IKONOS and WorldView-2 data show that the proposed fusion method is competitive or even superior to the other state-of-the-art fusion methods.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification
    • Authors: Chen; Y.;Zhao, X.;Lin, Z.;
      Pages: 1295 - 1305
      Abstract: In hyperspectral remote sensing image classification, ensemble systems with support vector machine (SVM), such as the Random Subspace SVM Ensemble (RSSE), have significantly outperformed single SVM on the robustness and overall accuracy. In this paper, we introduce a novel subspace mechanism, the Optimizing Subspace SVM Ensemble (OSSE), to improve RSSE by selecting discriminating subspaces for individual SVMs. The framework is based on Genetic Algorithm (GA), adopting the Jeffries–Matusita (JM) distance as a criterion, to optimize the selected subspaces. The combination of optimizing subspaces is more suitable for classification than the random one, at the same time having the ability to accommodate requisite diversity within the ensemble. The modifications have improved the accuracies of individual classifiers; as a result, better overall accuracies are present. Experiments on the classification of two hyperspectral datasets reveal that our proposed OSSE obtains sound performances compared with RSSE, single SVM, and other ensemble with GA to optimize SVM.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Energy-Efficient Time-of-Flight Estimation in the Presence of Outliers: A
           Machine Learning Approach
    • Authors: Apartsin; A.;Cooper, L.N.;Intrator, N.;
      Pages: 1306 - 1313
      Abstract: The time-of-flight (ToF) estimation problem is common in sonar, ultrasound, radar, and other remote sensing applications. The conventional ToF maximum-likelihood estimator (MLE) exhibits a rapid deterioration in the accuracy when the signal-to-noise ratio (SNR) falls below a certain threshold. This threshold effect emerges mostly due to appearance of outliers associated with the side lobes in the autocorrelation function of a narrowband source signal. In our previous work, we have introduced a bank of unmatched filters and biased ToF estimators derived using these filters. These biased estimators form a feature vector for training a classifier which, subsequently, is used for reducing the bias and the variance parts induced by outliers in the mean-square error (MSE) of the MLE. In this paper, we extend the above method by introducing an adaptive scheme for controlling the number of measurements (pulses) required to achieve a desired accuracy. We show that using the information provided by a classifier, it is possible to achieve the estimation error of the MLE but by using significantly less number of pulses and thus energy on average.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • A Support Vector Conditional Random Fields Classifier With a Mahalanobis
           Distance Boundary Constraint for High Spatial Resolution Remote Sensing
    • Authors: Zhong; Y.;Lin, X.;Zhang, L.;
      Pages: 1314 - 1330
      Abstract: In this paper, a modified conditional random fields (CRFs) classifier, namely the support vector conditional random fields classifier with a Mahalanobis distance boundary constraint (SVRFMC), is proposed to perform the task of classification for high spatial resolution (HSR) remote sensing imagery. In SVRFMC, the CRFs model has the intrinsic ability of incorporating the contextual information in both the observation and labeling fields. Support vector machine (SVM) is set as the spectral term to get a more precise estimation of each pixel’s probability of belonging to each possible class. To preserve the spatial details in the classification result, a Mahalanobis distance boundary constraint is considered as the spatial term to undertake appropriate spatial smoothing. By integrating SVM and a Mahalanobis distance boundary constraint, SVRFMC can not only avoid the explicit modeling of observed data, but can also undertake appropriate smoothing with the consideration of contextual information, thereby exhibiting more universality and validity in the application of HSR image classification, especially when the image has a complex land-cover class distribution and the training samples are limited. Three HSR images comprising QuickBird, IKONOS, and HYDICE imagery were utilized to evaluate the performance of the proposed algorithm in comparison to other image classification approaches: noncontextual multiclass SVM, a traditional object-oriented classifier (OOC), an object-oriented classification based on fractal net evolution approach (FNEA) segmentation (OO-FNEA), a simplified CRF model with boundary constraint (BC-CRF), and a recently proposed contextual classifier combining SVM and Markov random fields (Markovian support vector classifier). The experimental results demonstrate that the SVRFMC algorithm is superior to the other methods, providing a satisfactory classification result for HSR imagery, including both multispectral HSR imagery and hyperspectral HSR- imagery, even with limited training samples, from both the visualization and quantitative evaluations.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • The Angular and Spectral Kernel-Driven Model: Assessment and Application
    • Authors: You; D.;Wen, J.;Liu, Q.;Liu, Q.;Tang, Y.;
      Pages: 1331 - 1345
      Abstract: Land surface albedo is a critical parameter in the earth's energy budget. Multiple-sensor data contain more information than single-sensor data, enabling us to retrieve albedo more accurately. The Angular and Spectral Kernel driven model (ASK model), which introduces component spectra into a kernel-driven model, provides a way to combine multiple-sensor data to retrieve BRDF/albedo. The construction of the ASK model is detailed in Liu's paper. As a follow-up, this paper provides an extensive assessment of the ASK model and its application using multi-sensory data. The assessment is described in both angular and spectral dimensions using simulated datasets from ProSail, 5-Scale, and RGM. With the ability to combine information from the spectral and angular domains, the inversion of the ASK model requires fewer angular observations than the traditional model. Four angles are sufficient when combining seven MODIS bands. In the spectral dimension, the model performance reveals high numerical correlations among bands: the red and NIR bands are generally required to make a good spectra fitting, and adding an additional SWIR band can improve the performance. The synergistic retrieval of albedo combining FY3/VIRR, AVHRR, and MODIS shows a satisfactory agreement with in situ measurements, where the RMSE is 0.013 in the 4-day composited temporal resolution retrieval. The results show that the ASK model is promising for BRDF/albedo inversion using multi-sensor data, although it shows some dependence on the accuracy of the component spectra.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Assessing the Accuracy of Forest Cover Map for 1990, 2000 and 2010 at
           National Scale in Gabon
    • Authors: Fichet; L.-V.;Sannier, C.;Makaga, E.M.K.;Seyler, F.;
      Pages: 1346 - 1356
      Abstract: The Gabonese Agency for Space Studies and Observations (AGEOS) was set up in 2010 with one of its aims being to develop a national forest monitoring capability. In addition, the European Space Agency (ESA) has developed its activities in the Congo basin through the REDD extension of its GMES Service Element on Forest Monitoring program (GSE FM). The ESA GSE FM REDD extension project is seen by the Gabonese authorities as a precursor to the establishment of the AGEOS for the monitoring of forest cover. During this phase of the project, the production of forest area maps and forest cover change maps for 1990 and 2000 was initiated with a wall to wall approach for the total area of Gabon and about a third of the country for 2010. Initial results confirm the generally low level of deforestation expected in the Congo basin region and in Gabon in particular. However, these results cannot be used without a thorough and statistically sound assessment of thematic accuracy. Thus, a suitable sampling approach was developed to assess the accuracy and results show that these products exceed the requirements set by AGEOS with an overall accuracy above 95%.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Robust Locally Weighted Regression for Superresolution Enhancement of
           Multi-Angle Remote Sensing Imagery
    • Authors: Ma; J.;Chan, J.C.-W.;Canters, F.;
      Pages: 1357 - 1371
      Abstract: This paper presents a robust locally weighted least-squares kernel regression method for superresolution (SR) enhancement of multi-angle remote sensing imagery. The method is based on the concept of kernel-based regression, where the local image patch is approximated by an ${mbi{N}}$ -term Taylor series. To reduce the impact of high frequency noise on SR performance, a robust fitting procedure is adopted. The approach proposed is tested with simulated multi-angle data derived from panchromatic WorldView-2 imagery and with real multi-angle WorldView-2 remote sensing images.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • CUDA-Based SSA Method in Application to Calculating EM Scattering From
           Large Two-Dimensional Rough Surface
    • Authors: Jiang; W.-Q.;Zhang, M.;Wei, P.-B.;Yuan, X.-F.;
      Pages: 1372 - 1382
      Abstract: The small slop approximation (SSA) is an accurate method to calculate the electromagnetic (EM) scattering properties of rough surfaces. However, its computational complexity restricts its application to smaller domains and there is always the need for speedup in very large cases using pure central processing units (CPUs) hardware. With the development of graphics processing units (GPUs), more processors are dedicated to perform independent calculations. In addition, NVIDIA introduced a parallel computing platform, compute unified device architecture (CUDA), which provides researchers an easy way to use processors on GPU. To calculate EM scattering properties on GPU, we reformulate the SSA method with CUDA to take advantage of GPU threads. Because each thread executes synchronously and deals with a corresponding point data of rough surface, the CUDA-based SSA method calculates faster than the pure-CPU equivalent. To overcome memory limitations, the data of large rough surface are stored on hard disk. Moreover, a subsidiary thread is used to deal with the process of data transmission between the memory and the hard disk and reduce transmitting time further. The factors, block size, data transfers, and register, are also discussed in the optimization of the CUDA application. Test cases running on a NVIDIA GTX 460 GPU indicate that two orders of magnitude speedup, including file input and output, is obtained with our new formulation.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • ++-Band+Polarimetric+SAR+Imagery&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&;Li,++P.;Yang,++J.;Zhang,++J.;Lang,++F.;">A New Automatic Ship Detection Method Using
    -Band Polarimetric SAR
    • Authors: Wei; J.;Li, P.;Yang, J.;Zhang, J.;Lang, F.;
      Pages: 1383 - 1393
      Abstract: Ship–sea contrast can be improved significantly when the full polarimetric information is used, compared with the information provided by a single polarization channel. Therefore, a new automatic ship detection method, termed SPAN Wishart (SPWH), is proposed in this paper based on an unsupervised classification concept, which combines the SPAN of a polarimetric SAR (POLSAR) data with the complex Wishart classifier. The significant improvement of this technique is to utilize the SPAN of ship cluster center as an iterative termination criterion to realize the automatic ship detection. Then, another method based on multifrequency is proposed to discriminate between ships and their ambiguities to provide a substitute for the ground truth for the subsequent validation of the SPWH algorithm by using an AIRSAR polarimetric dataset, which consists of $C$ - and $L$ -band data covering Kojimawan in Tamano of Japan. After that, the SPWH is validated only using the $L$ -band data with the defined ships by comparing the performance of the SPWH with a traditional CFAR detector. By contrast, the SPWH algorithm is more effective, robust, and completely automatic.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Improving the Accuracy of Urban Land Cover Classification Using Radarsat-2
           PolSAR Data
    • Authors: Salehi; M.;Sahebi, M.R.;Maghsoudi, Y.;
      Pages: 1394 - 1401
      Abstract: Land cover classification is one of the most important applications of polarimetric SAR images, especially in urban areas. There are numerous features that can be extracted from these images, hence feature selection plays an important role in PolSAR image classification. In this study, three main steps are used to address this task: 1) feature extraction in the form of three categories, namely original data features, decomposition features, and SAR discriminators; 2) feature selection in the framework of the single and multi-objective optimization; and 3) image classification using the best subset of features. In single objective methods, we employ genetic algorithms (GAs) and support vector machines (SVMs) or multi-layer perceptron (MLP) neural network in order to maximize classification accuracy. Then a new method is proposed to perform an efficient land cover classification of the San Francisco Bay urban area based on the multi-objective optimization approach. The objectives are to minimize the error of classification and the number of selected PolSAR parameters. The experimental results on Radarsat-2 fine-quad data show that the proposed method outperforms the single objective approaches tested against it, while saving computational complexity. Finally, we show that the our method has a better performance than the SVM with full set of features and the Wishart classifier which is based on the covariance matrix.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • Effects of Inundated Vegetation on X-Band HH–VV Backscatter and
           Phase Difference
    • Authors: Zalite; K.;Voormansik, K.;Olesk, A.;Noorma, M.;Reinart, A.;
      Pages: 1402 - 1406
      Abstract: TerraSAR-X HH and HH–VV polarization data of a wetland in Estonia are used to study the effects of HH–VV phase difference on detecting inundated vegetation. Patches with different tree species composition are selected from two images representing flooded and unflooded conditions. Contribution of the double bounce mechanism is studied via phase difference and backscatter analysis. An increase in both HH and HH-VV backscatter was observed for all study cases in flooded conditions, as compared to unflooded periods. Over the different patches, HH backscatter increased from 2.3 dB to 8.2 dB, while for HH–VV — from 3 dB to 9.8 dB. The HH–VV channel allowed for a better separation between flooded and unflooded forest than HH, with the difference between the HH–VV and HH backscatter increase in inundated conditions ranging from 0.2 dB for coniferous stands with a tree height of less than 10 m to 1.6 dB for deciduous stands with a tree height of less than 10 m. A larger separation on both channels was in general observed in deciduous stands as the leaf-off season allowed for a better penetration depth. A considerable phase shift between HH and VV channels ranging from 10° to 28° was observed due to inundation, suggesting a strong contribution from the double bounce mechanism. For unflooded patches, the difference in backscatter between the two acquisition dates ranged from –0.8 dB to 1 dB for HH, and from 0 dB to 2 dB for HH–VV channel. Phase shift for unflooded patches varied from 5° to 8°.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
  • 2014 IEEE membership application
    • Pages: 1407 - 1408
      Abstract: 2014 IEEE membership application form.
      PubDate: April 2014
      Issue No: Vol. 7, No. 4 (2014)
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