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2008 IEEE International Geoscience and remote sensing Symposium - Proceedings

作者： Liu, Wei-Min Chang, Chein-I Remote Sensing Signal Image Processing Laboratory Dept. of CSEE University of Maryland Baltimore County United States

ISBN: (纸本)9781424428083

Anomaly detection is of particular interest in hyperspectral image analysis since many unknown and subtle signals which cannot be resolved by multispectral sensors can now be uncovered by hyperspectral imagers. More importantly, the signals of this type generally cannot be identified by visual assessment or prior knowledge and provide crucial and critical information for data analysis. Many anomaly detectors have been designed based on the most widely used anomaly detector developed by Reed and Yu, called RX detector (RXD). However, a key issue in making RX detector-like anomaly detectors successful is how to effectively utilize the information provided by the sample correlation, e.g., sample covariance matrix used by RXD. This paper develops a concept of designing anomaly detectors which includes RXD-like anomaly detectors as special cases. It is referred to as multiple-window anomaly detection (MWAD) which makes use of multiple windows with varying sizes to capture different levels of local spectral variations so that anomalous targets of various sizes can be characterized and interpreted by different window sizes. With this new MWAD, many interesting findings can be derived including the RXD-like anomaly detectors as its special cases. © 2008 IEEE.

关键词： Covariance matrix

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Brain Tissue Classification Using Independent Vector Analysis (IVA) for Magnetic Resonance image

Brain Tissue Classification Using Independent Vector Analysi...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yaw-Jiunn Chiou Hsiang-Min Chen Jyh Wen Chai Clayton Chi-Chang Chen Yen-Chieh Ouyang Wu-Chung Su Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University and Hospital Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and technology Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image.

关键词： Brain image analysis Magnetic analysis Magnetic resonance Independent component analysis Magnetic resonance imaging Support vector machines Signal analysis image resolution Signal resolution

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An update on SeaBED: A TesBED for validating subsurface aquatic hyperspectral remote sensing algorithms

An update on SeaBED: A TesBED for validating subsurface aqua...

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remote sensing of the Ocean, Sea Ice, and Large Water Regions 2008

作者： Goodman, James A. Vélez-Reyes, Miguel Rosario-Torres, Samuel Laboratory for Applied Remote Sensing and Image Processing Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems University of Puerto Rico at Mayagüez PO Box 9048 Mayagüez 00681 Puerto Rico

ISBN: (纸本)9780819473363

SeaBED is an integrated data set and testing infrastructure for researchers to validate subsurface aquatic remote sensing algorithms. The purpose behind developing SeaBED is to collect multiple levels of image, field, and laboratory data with which to validate physical models, inversion algorithms, feature extraction tools and classification methods for subsurface aquatic sensing using hyperspectral imaging. The focus of this testbed facility is a field site located on Enrique Reef in southwestern Puerto Rico. This field site, which includes a heterogeneous mixture of both coral reef and seagrass habitats, offers a well defined system for evaluating analysis techniques under natural environmental conditions. Data produced from the field site currently includes airborne, satellite, and field-level hyperspectral and multispectral images, in situ spectral signatures, and water bio-optical properties. This data provides a valuable combination of sensing imagery and fully characterized ground truth information for developing and validating remote sensing algorithms. A major accomplishment for SeaBED was the collection of high-resolution hyperspectral imagery and associated ground truth of the near shore reefs and coastal ecosystems in southwestern Puerto Rico in 2007. The mission included 1740 km2 of imagery acquired at 4 m spatial resolution, with 110 km 2 enhanced coverage of four science areas at 1, 2, 4 and 8 m spatial resolutions to facilitate investigation of spatial scaling issues and the testing of subsurface unmixing algorithms. We present an overview of SeaBED and also describe particulars of the 2007 data collection campaign, including both image acquisition and field data collection efforts. © 2008 Copyright SPIE - The International Society for Optical Engineering.

关键词： Spectrum analysis

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Block truncation signature coding for hyeprspectral analysis

Block truncation signature coding for hyeprspectral analysis

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Imaging Spectrometry XIII

作者： Chakravarty, Sumit 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)9780819473066

This paper introduces a new signature coding which is designed based on the well-known Block Truncation Coding (BTC). It comprises of bit-maps of the signature blocks generated by different threshold criteria. Two new BTC-based algorithms are developed for signature coding, to be called Block Truncation Signature Coding (BTSC) and 2-level BTSC (2BTSC). In order to compare the developed BTC based algorithms with current binary signature coding schemes such as Spectral Program Analysis Manager (SPAM) developed by Mazer et al. and Spectral Feature-based Binary Coding (SFBC) by Qian et al, three different thresholding functions, local block mean, local block gradient, local block correlation are derived to improve the BTSC performance where the combined bit-maps generated by these thresholds can provide better spectral signature characterization. Experimental results reveal that the new BTC-based signature coding performs more effectively in characterizing spectral variations than currently available binary signature coding methods.

关键词： Managers

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Improving Hyperspectral image Classification based on Graphs using Spatial Preprocessing

Improving Hyperspectral Image Classification based on Graphs...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Santiago Velasco-Forero Vidya Manian Laboratory for Applied Remote Sensing and Image Processing University of Puerto Rico Mayaguez Puerto Rico

Spatial smoothing over the original hyperspectral data based on wavelet and partial differential equations (PDEs) are incorporated in the classifiers using composite kernel with kNN graphs. The kernels combine spectral-spatial relationships using the smoothed and original images. Experiments with real hyperspectral scenarios are presented. Comparison with recent graph based methods show that the proposed scheme improves existing methods.

关键词： Hyperspectral imaging image classification Hyperspectral sensors Semisupervised learning Kernel Pixel remote sensing image processing Smoothing methods Partial differential equations

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Multiple-Window Anomaly Detection for Hyperspectral imagery

Multiple-Window Anomaly Detection for Hyperspectral Imagery

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Wei-min Liu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of CSEE University of Maryland Baltimore USA

关键词： Hyperspectral imaging Detectors Hyperspectral sensors image analysis image resolution Signal resolution image sensors Signal processing Data analysis Covariance matrix

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A pyramid-based block of skewers for pixel purity index for endmember extraction in hyperspectral imagery

A pyramid-based block of skewers for pixel purity index for ...

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作者： Chang, Chein-I Hsueh, Mingkai Liu, Weimin Wu, Chao-Cheng Chaudhry, Farzeen Solyar, Gregory Plaza, Antonio Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Computer Science Department University of Extremadura Avda. de la Universidad s/n 10.071 Caceres Spain

Pixel Purity Index (PPI) has been widely used for endmember extraction. Recently, an approach using blocks of skewers was proposed by Theiler et al., called blocks of skewers (BOS) method, to improve computation of the PPI. It utilizes a block of skewers to reduce number of calculations of dot products operated by the PPI on each skewers with all data sample vectors. Unfortunately, the BOS method also suffers from the same drawbacks that the PPI does in terms of several parameters which are needed to be determined a priori. Besides, it also has an additional parameter, block size, B needed to be determined where no guideline is provided of how to select this parameter. In this paper, the BOS method is also investigated. Most importantly, a new pyramid-based block design for the BOS method is also introduced as opposed to the cube-based block designed used by Theiler et al.'s BOS. One major advantage of our proposed pyramid-based BOS over Theiler et al.'s cube-design BOS is the hardware design for Field Programmable Gate Arrays (FPGAs) implementation. © 2008 World Scientific Publishing Company.

关键词： Pixels

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Method of boundary extraction based on schrödinger equation 21

Method of boundary extraction based on schrödinger equation

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2008 21st ISPRS International Congress for Photogrammetry and remote sensing

作者： Lou, Liantang Zhan, Xin Fu, Zhongliang Ding, Mingyue School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China School of Science Wuhan Institute of Technology Wuhan430073 China Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.

关键词： Initial value problems

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A PYRAMID-BASED BLOCK OF SKEWERS FOR PIXEL PURITY INDEX FOR ENDMEMBER EXTRACTION IN HYPERSPECTRAL imageRY

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International Journal of High Speed Electronics and Systems 2008年第2期18卷 469-482页

作者： CHEIN-I CHANG MINGKAI HSUEH WEIMIN LIU CHAO-CHENG WU FARZEEN CHAUDHRY GREGORY SOLYAR ANTONIO PLAZA Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 USA Computer Science Department University of Extremadura Avda. de la Universidad s/n 10.071 Caceres Spain

Pixel Purity Index (PPI) has been widely used for endmember extraction. Recently, an approach using blocks of skewers was proposed by Theiler et al., called blocks of skewers (BOS) method, to improve computation of the PPI. It utilizes a block of skewers to reduce number of calculations of dot products operated by the PPI on each skewers with all data sample vectors. Unfortunately, the BOS method also suffers from the same drawbacks that the PPI does in terms of several parameters which are needed to be determined a priori . Besides, it also has an additional parameter, block size, B needed to be determined where no guideline is provided of how to select this parameter. In this paper, the BOS method is also investigated. Most importantly, a new pyramid-based block design for the BOS method is also introduced as opposed to the cube-based block designed used by Theiler et al.'s BOS. One major advantage of our proposed pyramid-based BOS over Theiler et al.'s cube-design BOS is the hardware design for Field Programmable Gate Arrays (FPGAs) implementation.

关键词： Blocks of skewers (BOS) method Field Programmable Gate Arrays (FPGAs) Pixel purity index (PPI)

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Updated radiometric calibration for the Landsat-5 thematic mapper reflective bands

Updated radiometric calibration for the Landsat-5 thematic m...

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作者： Helder, Dennis L. Markham, Brian L. Thome, Kurtis J. Barsi, Julia A. Chander, Gyanesh Malla, Rimy Department of Electrical Engineering and Computer Science South Dakota State University Brookings SD 57007 United States Landsat Project Science Office NASA Goddard Space Flight Center Greenbelt MD 20771 United States Remote Sensing Group Optical Sciences Center University of Arizona Tucson AZ 85721 United States Science Systems and Applications Inc. NASA Goddard Space Flight Center Greenbelt MD 20771 United States Science Applications International Corporation U.S. Geological Survey Center for Earth Resources Observation and Science Sioux Falls SD 57198 United States Image Processing Laboratory South Dakota State University Brookings SD 57007 United States

The Landsat-5 Thematic Mapper (TM) has been the workhorse of the Landsat system. Launched in 1984, it continues collecting data through the time frame of this paper. Thus, it provides an invaluable link to the past history of the land features of the Earth's surface, and it becomes imperative to provide an accurate radiometric calibration of the reflective bands to the user community. Previous calibration has been based on information obtained from prelaunch, the onboard calibrator, vicarious calibration attempts, and cross-calibration with Landsat-7. Currently, additional data sources are available to improve this calibration. Specifically, improvements in vicarious calibration methods and development of the use of pseudoinvariant sites for trending provide two additional independent calibration sources. The use of these additional estimates has resulted in a consistent calibration approach that ties together all of the available calibration data sources. Results from this analysis indicate a simple exponential, or a constant model may be used for all bands throughout the lifetime of Landsat-5 TM. Where previously time constants for the exponential models were approximately one year, the updated model has significantly longer time constants in bands 1-3. In contrast, bands 4, 5, and 7 are shown to be best modeled by a constant. The models proposed in this paper indicate calibration knowledge of 5% or better early in life, decreasing to nearly 2% later in life. These models have been implemented at the U.S. Geological Survey Earth Resources Observation and Science (EROS) and are the default calibration used for all Landsat TM data now distributed through EROS. © 2008 IEEE.

关键词： Landsat

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