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Communications in Computer and Information Science 2014年 437卷 336-344页

作者： Cheng, Yan Jiang, Zhiguo Shi, Jun Zhang, Haopeng Meng, Gang Image Processing Center School of Astronautics Beihang University Beijing China Beijing Key Laboratory of Digital Media Beijing China Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9783662454978

In this paper we propose an unsupervised approach based on lowrank representation (LRR) for change detection in remote sensing images. Given a pair of remote sensing images obtained from the same area but in different time, the subtraction and logarithm ratio operators are firstly applied to obtain two difference images. Meanwhile the sparse part generated by LRR is also employed for acquiring another difference image, which can detect the change information. Afterwards, LRR is used again to obtain the low-rank part of these three difference images which can reflect the common characteristics. Finally k-means is performed on the low-rank part and thus the final result of change detection can be gained. Experimental results show the effectiveness and feasibility of the proposed method. © Springer-Verlag Berlin Heidelberg 2014.

关键词： remote sensing

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DOMAIN ADAPTATION WITH HIDDEN MARKOV RANDOM FIELDS

DOMAIN ADAPTATION WITH HIDDEN MARKOV RANDOM FIELDS

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IEEE International Geoscience and remote sensing Symposium

作者： Jan-Pieter Jacobs Guy Thoonen Devis Tuia Gustavo Camps-Valls Birgen Haest Paul Scheunders iMinds-Vision Lab University of Antwerp LaSIG laboratory Ecole Polytechnique Federale de Lausanne Image Processing Laboratory (IPL) Universitat de Valencia VITO-TAP Remote Sensing Department Flemish Institute for Technological Research

ISBN: (纸本)9781479911127

In this paper, we propose a method to match multitemporal sequences of hyperspectral images using Hidden Markov Random Fields. Based on the matching of the data manifold, the algorithm matches the reflectance spectra of the classes, thus allowing the reuse of labeled examples acquired on one image to classify the other. This allows valorization of spectra collected in situ to other acquisitions than the one they were acquired for, without user supervision, prior knowledge of the class reflectance in the new domain or global information about atmospheric conditions.

关键词： Multitemporal classification Domain adaptation Graph matching Hidden Markov Random Fields

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Relationship exploration among PPI, ATGP and VCA via theoretical analysis

Relationship exploration among PPI, ATGP and VCA via theoret...

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作者： Chang, Chein-I. Wen, Chia-Hsien Wu, Chao-Cheng Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland 1000 Hilltop Circle Baltimore MD 21250 United States Department of Computer Science and Information Engineering Providence University Taichung Taiwan Department of Computer Science and Information Management Providence University 200 Taiwan Boulevard Shalu Dist. Taichung City 43301 Taiwan Department of Electrical Engineering National Taipei University of Technology 1 Zhongxiao E. Rd. Taipei 106 Taiwan

The principle of orthogonality using orthogonal projection (OP) is the key concept to develop mean squared error estimation theory in signal processing and communications. It has also found its way in a wide variety of applications in hyperspectral imaging. This paper explores close relationships among three OP-based algorithms, pixel purity index (PPI), vertex component analysis (VCA) in endmember extraction, automatic target generation process (ATGP) in mixed pixel classification and target detection, etc. and further shows that they indeed can be interpreted one way or another. Specifically, VCA and ATGP are essentially the same algorithm in the sense that they both extract targets via maximal OP from a nested sequence of successive orthogonal subspace projection spaces. Copyright © 2013 Inderscience Enterprises Ltd. Copyright © 2013 Inderscience Enterprises Ltd.

关键词： Hyperspectral imaging

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Unmixing analysis based on multiscale segmentation

Unmixing analysis based on multiscale segmentation

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Workshop on Hyperspectral image and Signal processing: Evolution in remote sensing, WHISPERS

作者： Maria C. Torres-Madronero Miguel Vélez-Reyes Laboratory for Applied Remote Sensing and Image Processing University of Puerto Rico-Mayaguez PR

Unmixing plays an important role in hyperspectral image processing and the wide range of application of hyperspectral data. Fully-automated techniques that take into account the spatial and spectral information captured by the hyperspectral sensor are required. This paper describes a new approach for unmixing analysis of hyperspectral imagery using a novel multiscale segmentation technique for the estimation of both the number of endmembers and their spectral signatures. The proposed approach uses the spatial and spectral information of hyperspectral imagery. In addition, the described method seeks to model the natural variability of the materials using multiple spectral to build endmember classes. Experimental results using an image from SOC 700 hyperspectral imagery are presented.

关键词： image segmentation Materials Estimation Hyperspectral imaging Abstracts Indexes

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Modified full abundance-constrained spectral unmixing

Modified full abundance-constrained spectral unmixing

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High-Performance Computing in remote sensing II

作者： Wong, Englin Chang, Chein-I Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819492791

Abundance fully constrained least squares (FLCS) method has been widely used for spectral unmixing. A modified FCLS (MFCLS) was previously proposed for the same purpose to derive two iterative equations for solving fully abundance-constrained spectral unmixing problems. Unfortunately, its advantages have not been recognized. This paper conducts a comparative study and analysis between FCLS and MFCLS via custom-designed synthetic images and real images to demonstrate that while both methods perform comparably in unmixing data, MFCLS edges out FCLS in less computing time. © 2012 SPIE.

关键词： Iterative methods

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RADIOMETRIC INTERCALIBRATION OF THE MICROWAVE HUMIDITY SOUNDER ON NOAA-18, METOP-A, AND NOAA-19 USING SAPHIR ON MEGHA-TROPIQUES

RADIOMETRIC INTERCALIBRATION OF THE MICROWAVE HUMIDITY SOUND...

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IEEE International Geoscience and remote sensing Symposium

作者： Linwood Jones Saswati Datta Andrea Santos-Garcia James R. Wang Vivienne Payne Nicholas Viltard Thomas Wilheit Central Florida Remote Sensing Lab University of Central Florida Orlando FL 32816-2362 Data and Image Processing Consultants Morrisville NC 27560 Science Systems and Applications Inc. Lanham MD 20706 Jet Propulsion Laboratory California Institute of Technology Pasadena CA91109 Laboratorie Atmospheres Milieux Observations Spatiales University of Paris Paris France Texas A&M University Ashville NC

ISBN: (纸本)9781479911127

The purpose of this paper is to ascertain the use of SAPHIR (in a low earth orbit) for radiometric brightness temperature, Tb, intercalibration of sounder channel sensors (in near polar orbits) within the context of the Global Precipitation Measurement (GPM) mission [1]. For this purpose, we present results of the radiometric intercalibration based on the double differences technique between SAPHIR and the Microwave Humidity Sounder (MHS) on the MetoOp-A, NOAA-18 and NOAA-19 polar orbiting satellites. The analysis presented was reported at the last intercalibration meeting at Toulouse, France, and were performed by Texas A&M University (TAMU), Science Systems and Applications (SSA) and, Central Florida remote sensing Lab (CFRSL) at University of Central Florida.

关键词： calibration validation sounder SAPHIR MHS MetOp-A NOAA-18 NOAA-19 Sapphire Acoustic generators Calibration Polar orbits Central Florida intercalibration moistening

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SAR image Multiclass Segmentation Using a Multiscale TMF Model in Wavelet Domain

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IEEE Geoscience and remote sensing Letters 2012年第6期9卷 1099-1103页

作者： Peng Zhang Ming Li Yan Wu Ming Liu Fan Wang Lu Gan National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Remote Sensing Image Processing and Fusion Group Xidian University Xi'an China

The triplet Markov field (TMF) model recently proposed is suitable for dealing with nonstationary synthetic aperture radar (SAR) image segmentation. In this letter, we propose a multiscale TMF model in wavelet domain, named as the wavelet-domain TMF (WTMF) model. In the WTMF model, a multiscale causal WTMF energy function is constructed to capture the intra- and interscale dependences in random fields (X, U). Moreover, multiscale likelihoods of the WTMF model are derived based on a wavelet hidden Markov tree to capture the statistical properties of wavelet coefficients. The proposed model can integrate the global and local information in terms of spatial configuration and image features in a more complete manner. The coarser scale information is utilized to guide the finer scale segmentation, and the coarseto-fine causal interactions are considered using a Markov chain. Experimental results prove that the proposed model can segment SAR images better than several models previously proposed.

关键词： image segmentation Hidden Markov models Computational modeling Markov processes Speckle Numerical models Noise

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DISCOVERING SINGLE CLASSES IN remote sensing imageS WITH ACTIVE LEARNING

DISCOVERING SINGLE CLASSES IN REMOTE SENSING IMAGES WITH ACT...

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IEEE International Geoscience and remote sensing Symposium

作者： M. Furlani D. Tuia J. Munoz-Mari F. Bovolo G. Camps-Valls L. Bruzzone Remote Sensing Laboratory University Trento Laboratory of Geographic Information Systems Lausanne EPFL Image Processing Laboratory University of Valencia

ISBN: (纸本)9781467311601

When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. Moreover, the efficiency of the search is also an issue, since the samples labeled as background are not used by target detectors such as the support vector data description (SVDD). In this work we propose a competitive and effective approach to identify the most relevant training samples for one-class classification based on the use of an active learning strategy. The SVDD classifier is first trained with insufficient target examples. It is then used to detect the most informative samples to be labeled by a user through active learning techniques. By selecting unlabeled samples in a smart way and by adopting a diversity criterion, it is possible to obtain an accurate description of the class of interest with a relatively small number of training samples. The performance of the proposed method is illustrated in a change detection scenario and is validated by comparison with state-of-art active learning techniques originally developed for multiclass problems.

关键词： Experiential Learning Exploration support vector data description remote sensing Phyla images Object detection

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SEMISUPERVISED NONLINEAR FEATURE EXTRACTION FOR image CLASSIFICATION

SEMISUPERVISED NONLINEAR FEATURE EXTRACTION FOR IMAGE CLASSI...

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IEEE International Geoscience and remote sensing Symposium

作者： Emma Izquierdo-Verdiguier Luis Gomez-Chova Lorenzo Bruzzone Gustavo Camps-Valls Image Processing Laboratory (IPL). Universitat de Valencia Remote Sensing Laboratory Dept. of Information Engineering and Computer Science. University of Trento

ISBN: (纸本)9781467311601

Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can significantly improve data description by defining an effective semisupervised nonlinear feature extraction strategy. We present a novel semisupervised Kernel Partial Least Squares (KPLS) algorithm for non-linear feature extraction. The method relies on combining two kernel functions: the standard RBF kernel using labeled information and a generative kernel directly learned by clustering the data. The effectiveness of the proposed method is successfully illustrated in multi- and hyper-spectral remote sensing image classification: accuracy improvements between +15 - 20% over standard PCA and +10% over advanced kernel PCA and KPLS for both images is obtained. Matlab code is available at for the interested readers.

关键词： Classification Feature extraction Kernel methods Partial least squares (PLS) Generative kernels Feature extraction image classification images Seeds Data relationships principal components analysis Data description Partial least squares Kernel hyperspectral remote sensing

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Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model

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Pattern Recognition 2012年第11期45卷 4018-4033页

作者： Peng Zhang Ming Li Yan Wu Lu Gan Ming Liu Fan Wang Gaofeng Liu National Key Laboratory of Radar Signal Processing Xidian University Xi'an 710071 China Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University P.O. Box 140 Xi'an Shaanxi 710071 China

Triplet Markov fields (TMF) model proposed recently is suitable for nonstationary image segmentation. For synthetic aperture radar (SAR) image segmentation, TMF model can adopt diverse statistical models for SAR data related to diverse radar backscattering sources. However, TMF model does not take into account the inherent imprecision associated with SAR images. In this paper, we propose a statistical fuzzy TMF (FTMF) model, which is a fuzzy clustering type treatment of TMF model, for unsupervised multi-class segmentation of SAR images. This paper contributes to SAR image segmentation in four aspects: (1) Nonstationarity of the statistical distribution of SAR intensity/amplitude data is taken into account to improve the spatial modeling capability of fuzzy TMF model. (2) Mean field theory is generalized to deal with planar variables to derive prior probability in fuzzy TMF model, which resolves the problem in Gibbs sampler in terms of computation cost. (3) A fuzzy objective function with regularization by Kullback–Leibler information of fuzzy TMF model is constructed for SAR image segmentation. The introduction of fuzziness for the belongingness of SAR image pixel makes fuzzy TMF model be able to retain more information from SAR image. (4) Fuzzy iterative conditional estimation (ICE) method, as an extension of the general ICE method is proposed to perform the model parameters estimation. The effectiveness of the proposed algorithm is demonstrated by application to simulated data and real SAR images.

关键词： SAR image Multi-class segmentation Fuzzy triplet Markov field (FTMF) Fuzzy clustering Fuzzy objective function Fuzzy iterative conditional estimation

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