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Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery

Component analysis-based unsupervised linear spectral mixtur...

引用

WHISPERS '09 - 1st Workshop on Hyperspectral image and Signal processing: Evolution in remote sensing

作者： Jiao, Xiaoli Du, Yingzi Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratry Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD 21228 United States Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN 46202 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9781424446872

One of the most challenging issues in unsupervised linear spectral mixture analysis (LSMA) is how to obtain unknown knowledge of target signatures referred to as virtual endmembers (VEs) directly from the data to be processed. This issue has never arisen in supervised LSMA where the VEs are either assumed to be known a priori or can be provided by visual inspection. With the recent advent of hyperspectral sensor technology many unknown and subtle signal sources can be uncovered and revealed without prior knowledge. This paper addresses this issue and develops a component analysis-based unsupervised LSMA where the desired VEs can be extracted by component analysis-based transforms directly from the data to be processed without appealing for prior knowledge. In order to substantiate the utility of the proposed approach extensive experiments are conducted for demonstration. © 2009 IEEE.

关键词： Mixtures

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A wind and rain backscatter model derived from AMSR and SeaWinds data

A wind and rain backscatter model derived from AMSR and SeaW...

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作者： Nielsen, Seth N. Long, David G. Microwave Earth Remote Sensing Laboratory Electrical and Computer Engineering Department Brigham Young University Provo UT 84602 United States

The SeaWinds scatterometer was originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross section. Rain can degrade scatterometer wind estimation;however, the simultaneous wind/ rain (SWR) algorithm was developed to enable SeaWinds to simultaneously retrieve wind and rain rate data. This algorithm is based on colocating data from the Precipitation Radar on the Tropical Rainfall Measuring Mission and SeaWinds on QuikSCAT. This paper develops a new wind and rain radar backscatter model for SWR using colocated data from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds aboard the Advanced Earth Observing Satellite n. This paper accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. The performance of SWR using the new wind/rain model is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and National Center for Environmental Prediction numerical weather prediction winds. The new SWR algorithm produces more accurate rain estimates and improved winds, and detects rain with a low false alarm rate. © 2008 IEEE.

关键词： Rain

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Interest point detection for hyperspectral imagery

Interest point detection for hyperspectral imagery

引用

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XV

作者： Dorado-Munoz, Leidy P. Velez-Reyes, Miguel Roysam, Badrinath Mukherjee, Amit Center for Subsurface Sensing and Imaging Systems Laboratory for Applied Remote Sensing and Image Processing University of Puerto Rico at Mayaguez Mayaguez PR.00681-9048 United States Center for Subsurface Sensing and Imaging Systems Department of Electrical Computer and Systems Engineering Rensseleaer Polytechnic Institute Troy NY. 12180-3590 United States

ISBN: (纸本)9780819476005

This paper presents an algorithm for automated extraction of interest points (IPs)in multispectral and hyperspectral images. Interest points are features of the image that capture information from its neighbours and they are distinctive and stable under transformations such as translation and rotation. Interest-point operators for monochromatic images were proposed more than a decade ago and have since been studied extensively. IPs have been applied to diverse problems in computer vision, including image matching, recognition, registration, 3D reconstruction, change detection, and content-based image retrieval. Interest points are helpful in data reduction, and reduce the computational burden of various algorithms (like registration, object detection, 3D reconstruction etc) by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. An interest operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, stable under geometric transformation, and interpretable which are good candidates for interest points. Our approach extends ideas from Lowe's keypoint operator that uses local extrema of Difference of Gaussian (DoG) operator at multiple scales to detect interest point in gray level images. The proposed approach extends Lowe's method by direct conversion of scalar operations such as scale-space generation, and extreme point detection into operations that take the vector nature of the image into consideration. Experimental results with RGB and hyperspectral images which demonstrate the potential of the method for this application and the potential improvements of a fully vectorial approach over band-by-band approaches described in the literature. © 2009 SPIE.

关键词： Object detection

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Comparison of spectral-spatial classification for urban hyperspectral imagery with high resolution

Comparison of spectral-spatial classification for urban hype...

引用

2009 Joint Urban remote sensing Event

作者： He, Yang Ben, Ma Qian, Du Liangpei, Zhang Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS United States State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (纸本)9781424434619

For urban hyperspectral imagery with high spatial resolution, both spectral and spatial information are important and should be combined together to improve classification accuracy. In this paper, different combination strategies are investigated. In particular, a two-stage algorithm is developed where the pixel shape index (PSI)-based features are extracted as low level spatial features which are combined with dimensionality-reduced spectral features as inputs to a support vector machine (SVM) for classification. Then the resulting classification is refined with high level class spatial neighborhood information to further improve the classification accuracy. The preliminary result shows the effectiveness of this two-stage algorithm. ©2009 IEEE.

关键词： Classification (of information)

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Kernel-based Linear Spectral Mixture Analysis for hyperspectral image classification

Kernel-based Linear Spectral Mixture Analysis for hyperspect...

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

作者： Keng-Hao Liu Englin Wong Chein-I Chang Remote Sensing Signal and Image Processing Laboratory the Department of Computer Science and Electrical Engineering Department University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.

关键词： image analysis Spectral analysis Hyperspectral imaging image classification Least squares methods Hyperspectral sensors Kernel Subspace constraints remote sensing image resolution

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Automatic segmentation of brain structures using geometric moment invariants and artificial neural networks

Automatic segmentation of brain structures using geometric m...

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21st International Conference on Information processing in Medical Imaging, IPMI 2009

作者： Jabarouti Moghaddam, Mostafa Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI United States

ISBN: (纸本)3642024971

We propose an automatic method for the segmentation of the brain structures in three dimensional (3D) Magnetic Resonance images (MRI). The proposed method consists of two stages. In the first stage, we represent the shape of the structure using Geometric Moment Invariants (GMIs) in 8 scales. For each scale, an Artificial Neural Network (ANN) is designed to approximate the signed distance function of a desired structure. The GMIs along with the voxel intensities and coordinates are used as the input features of the ANN and the signed distance function as its output. In the second stage, we combine the outputs of the ANNs of the first stage and design another ANN to classify the image voxels into two classes, inside or outside of the structure. We introduce a fast method for moment calculations. The proposed method is applied to the segmentation of caudate, putamen, and thalamus in MRI where it has outperformed other methods in the literature. © 2009 Springer Berlin Heidelberg.

关键词： Neural networks

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Color quantization using principal components for initialization of Kohonen SOFM

Color quantization using principal components for initializa...

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IEEE International Conference on image processing

作者： Dimitris Mavridis Nikos Papamarkos Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Greece

A new method is proposed for initializing Kohonen's self-organizing feature maps (SOFM) of fixed zero neighborhood radius for use in color quantization. The method employs the two largest principal components of the input image so that the initial weights of a number of neurons approach the input image color distribution. The rest of the neurons are initialized using the smallest principal component of the input image. Namely, standard SOFM is applied to the projection of the input image pixels onto the plane spanned by the two largest principal components and to pixels of the original image defined by the smallest principal component. The neuron values which emerge initialize the final SOFM of fixed zero neighborhood radius that performs the color quantization of the original image. Experimental results show that the proposed method can often produce smaller quantization errors than standard SOFM and other color quantization methods.

关键词： Quantization Neurons Neural networks Pixel image color analysis image processing Clustering algorithms Fuzzy neural networks Vectors Lattices

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Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery

Component analysis-based unsupervised linear spectral mixtur...

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

作者： Xiaoli Jiao Yingzi Du Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science & Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

关键词： image analysis Spectral analysis Hyperspectral imaging Independent component analysis Personal communication networks Inspection Statistics Signal analysis Hyperspectral sensors Principal component analysis

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Interface-based hierarchy for synchronous data-flow graphs

Interface-based hierarchy for synchronous data-flow graphs

引用

IEEE Workshop on Signal processing Systems (SIPS)

作者： Jonathan Piat Shuvra S. Bhattacharyya Mickael Raulet IETRIINSA UMR CNRS 6164 Image and Remote Sensing laboratory Rennes France Department of Electrical and Computer Engineering University of Maryland College Park MD USA

Dataflow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of dataflow, termed synchronous dataflow (SDF), offers strong compile-time predictability properties, but has limited expressive power. In this paper we propose a new type of hierarchy in the SDF domain allowing more expressivity while maintaining its predictability. This new hierarchy semantic is based on interfaces that fix the number of tokens consumed/produced by a hierarchical vertex in a manner that is independent or separate from the specified internal dataflow structure of the encapsulated subsystem. This interface-based hierarchy gives the application designer more flexibility in iterative construction of hierarchical representations, and experimentation with different optimization choices at different levels of the design hierarchy.

关键词： Flow graphs Application software computer interfaces Power engineering computing Digital signal processing Data mining Processor scheduling computer architecture Data flow computing remote sensing

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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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2009 9th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2009

作者： Chiou, Yaw-Jiunn Chen, Hsiang-Min Chai, Jyh Wen Chen, Clayton Chi-Chang Ouyang, Yen-Chieh Su, Wu-Chung Yang, Ching-Wen Lee, San-Kan Chang, Chein-I. Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Computer Center Taichung Veterans General Hospital Taichung 407 Taiwan 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)9780769536569

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. © 2009 IEEE.

关键词： Magnetic resonance imaging

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