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Linearly constrained minimum variance beamforming approach to target detection and classification for hyperspectral imagery

Linearly constrained minimum variance beamforming approach t...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chein-I Chang Hsuan Ren Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

Subspace projection and maximum likelihood have been used for hyperspectral image classification. One of their disadvantages is the requirement of a priori knowledge about material signatures resident in images which may be difficult to obtain in many practical applications. In order to resolve this problem, an approach, called Constrained Energy Minimization (CEM) was developed for target detection where the only required knowledge is the target signature of interest. It was derived based on the Minimum Variance Distortionless Response (MVDR) in array processing. However, CEM has been also shown to be very sensitive to the knowledge being used to describe the target signature. This paper develops a more general approach, called Linearly Constrained Minimum Variance (LCMV) beamforming which includes CEM as a special case. Unlike CEM which considers a single constraint, LCMV uses multiple signatures to extend the capability of CEM to various target classification. More specifically, LCMV can be implemented as an estimator for target classification while CEM can be used as a detector for target detection.

关键词： Array signal processing Object detection Finite impulse response filter Hyperspectral sensors Hyperspectral imaging Nonlinear filters Image classification Vectors Power generation Layout

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Coding distortion caused by a phase difference between the LP filter and its residual

Coding distortion caused by a phase difference between the L...

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IEEE Workshop on speech Coding Proceedings

作者： M. Tammi V.T. Ruoppila S. Kuusisto J. Saarinen Signal Processing Laboratory Tampere University of Technology Tampere Finland Department of Electrical and Computer Engineering University of Sherbrooke QUE Canada

Several speech coding algorithms modify the time scale of the residual signal to facilitate efficient coding of pitch information. Time scaling, however, results in a phase difference between the coded residual signal and the time-variant linear prediction (LP) filter used for synthesis in the decoder. In this paper, we examine the coding distortion induced by this phase difference. Moreover, we show that it may cause audible artifacts to the synthesized speech even if lossless coding of all parameters is employed. These artifacts occur particularly at onsets when the frequency response of successive LP filters changes rapidly. A waveform interpolation coder is used to illustrate the effects of the phase mismatch.

关键词： Phase distortion Filters speech synthesis signal synthesis speech coding speech analysis Interpolation signal processing algorithms Filtering signal analysis

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Analysis of stochastic gradient identification of Hermite polynomial systems with memory

Analysis of stochastic gradient identification of Hermite po...

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International Symposium on signal processing and Its Applications (ISSPA)

作者： P. Celka N.J. Bershad J.M. Vesin Department of Electrical Engineering Signal Processing Laboratory Swiss Federal Institute of Technology Lausanne Switzerland Department of Electrical and Computer Engineering School of Engineering University of California Irvine CA USA

This paper presents analytical and Monte Carlo results for a stochastic gradient adaptive scheme which identifies an orthogonal polynomial-type nonlinear system with memory. The analysis includes recursions for the mean and fluctuation behavior of the adaptive parameters as well computation of the mean-square error increase due to the adaptation process.

关键词： Stochastic systems Polynomials Fluctuations Nonlinear filters Adaptive signal processing Taylor series Stochastic processes signal processing algorithms Vectors Australia

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Spectral information divergence for hyperspectral image analysis

Spectral information divergence for hyperspectral image anal...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

The authors propose an information theoretic criterion, called spectral information divergence (SID) for spectral similarity and discriminability. It is derived from the concept of divergence arising in information theory and can be used to describe the statistics of a spectrum. Unlike spectral angle mapper (SAM) which extracts geometric features between two spectra, SID views each pixel spectrum as a random variable and then measures the discrepancy of probabilistic behaviors between two spectra. In order to evaluate SID, SAM is used for comparison via hyperspectral data. Experimental results show that SID can characterise spectral similarity and variability more effectively than SAM.

关键词： Hyperspectral imaging Image analysis Hyperspectral sensors Random variables Remote sensing Information theory Pixel Data mining Interference Pattern classification

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Design and implementation of narrow-band linear-phase FIR filters with piecewise polynomial impulse response

Design and implementation of narrow-band linear-phase FIR fi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： T. Saramaki S.K. Mitra Signal Processing Laboratory Tampere University of Technology Tampere Finland Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

The transfer function of these filters is a parallel connection of several branches with transfer functions of the form G/sub l/(z/sup L/)F/sub l/(z), where the length of each F/sub l/(z) is L. It is shown that by properly selecting the F/sub l/(z)'s the overall impulse response can be divided into several blocks of L samples in such a manner that in each block the impulse response is a polynomial of a given degree. The degree is one less than the number of branches, whereas the overall length of the impulse response is N=ML, where M is the length of the G/sub l/(z)'s. Efficient implementation forms are proposed for the overall filter. Examples are included illustrating the efficiency of the proposed approach in implementing narrow-band linear-phase FIR filters.

关键词： Finite impulse response filter Narrowband Polynomials Transfer functions IIR filters Bandwidth Frequency signal processing Laboratories Digital filters

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An interference rejection-based radial basis function neural network for hyperspectral image classification

An interference rejection-based radial basis function neural...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qian Du Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

A new application for RBF neural networks in nonlinear mixed pixel classification for hyperspectral imaging is presented. It is a three-layer neural network with the input layer specified by spectral signatures of a mixed pixel vector, the hidden layer used for nonlinear mixing functions and the output layer used to produce classification results of the mixed pixel vector. A noise estimation method in conjunction with noise subspace projection is developed to reliably estimate the member of mixing materials plus interference signatures that can be used as the number of hidden nodes as well as the member of input nodes. The least-mean-square learning algorithm is applied to adjust parameters used in the hidden layer and weights of the output layers adaptively and simultaneously so as to achieve best possible performance. The performance is evaluated through a series of experiments via AVIRIS data. A comparative analysis is also conducted among various methods.

关键词： Interference Radial basis function networks Hyperspectral imaging Hyperspectral sensors Pixel Image classification Remote sensing Laboratories Image sensors Spectroscopy

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Wavelet-based image coder with channel-optimized trellis-coded quantization

Wavelet-based image coder with channel-optimized trellis-cod...

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International Conference on Acoustics, speech, and signal processing (ICASSP)

作者： Tuyet-Trang Lam G.P. Abousleman L.J. Karam Telecom. Research Center Department of Electrical Engineering Arizona State University Tempe AZ USA Speech and Signal Processing Laboratory Motorola SSG Scottsdale AZ USA

This paper presents a wavelet-based image coder optimized for transmission over binary symmetric channels (BSC). The proposed coder uses a channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder, and does not include any channel coding for error protection. Consequently, the proposed channel-optimized image coder is especially suitable for wireless transmission due to its reduced complexity. Furthermore, the improvement over TCQ-based image coders is significant. Examples are presented to illustrate the performance of the proposed COTCQ-based image coder.

关键词： Costs Telecommunications Image coding Channel coding Vector quantization speech processing signal processing Design optimization Protection Image reconstruction

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Round-off error free fixed-point design of polynomial FIR predictors

Round-off error free fixed-point design of polynomial FIR pr...

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Asilomar Conference on signals, Systems & computers

作者： J.M.A. Tanskanen V.S. Dimitrov Institute of Intelligent Power Electronics Department of Electrical and Communications Engineering Helsinki University of Technology Espoo Finland Laboratory of Signal Processing and Computer Technology Department of Electrical and Communications Engineering Helsinki University of Technology Espoo Finland

We present a novel method for designing polynomial FIR predictors for fixed-point environments. Our method yields filters that perform exact prediction of polynomial signals even with short coefficient word lengths. Under ordinary coefficient truncation or rounding, the prediction capability degrades, or may be totally lost. With the proposed method, the filters are designed so that the predictive properties are exactly preserved in fixed-point implementations. The proposed filter design method is based on integer programming (IP) and can be directly applied to any fixed-point FIR design specifications which can be formulated in a form of linear constraints on the filter coefficients.

关键词： Roundoff errors Polynomials Finite impulse response filter Design methodology Design engineering Power engineering and energy Degradation Nonlinear filters Filtering White noise

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OBE set estimates in classification problems

OBE set estimates in classification problems

引用

1998 Midwest Symposium on Circuits and Systems, MWSCAS 1998

作者： Joachim, D. Deller, J.R. Mandour, G.I. Department of Electrical and Computer Engineering Processing Laboratory Michigan State University 2120 EB Speech East LansingMI48824-1226 United States

ISBN: (纸本)0818689145

This paper explores the use of alternative estimates arising from the feasibility set of an optimal bounded ellipsoid (OBE) algorithm. The central estimator is interpretable as a least squares result, but all others in the bounding set are consistent with the observations and error bounds as well. The purpose of the present paper is to focus attention on the set estimates in an effort to stimulate further research into their utility in applications. As an example, we suggest the use of set estimates in classification problems in which the classes can be represented by distinct linear-in-parameters models, or by related sets. © 1999 IEEE.

关键词： Error analysis

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Generalized orthogonal subspace projection approach to multispectral image classification 4

Generalized orthogonal subspace projection approach to multi...

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Image and signal processing for Remote Sensing IV 1998

作者： Ren, Hsuan Chang, Chein-I Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States

ISBN: (纸本)9780819429599

Orthogonal subspace projection (OSP) has been successfully applied to hyperspectral image processing. In order for OSP to be effective, the number of bands must be no less than that of signatures to be classified so that there are sufficient dimensions to accommodate individual signatures to discriminate one another via orthogonal projection. This intrinsic constraint is not an issue for hyperspectral images since they generally have hundreds of bands which are more than the number of signatures resident within images. It, however, may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands such as 3-band SPOT images. This paper presents a generalization of OSP, called generalized OSP (GOSP) to relax this constraint in such a fashion that OSP can be extended to multispectral image processing in an unsupervised fashion. The idea of GOSP is to create new additional band images nonlinearly from original multispectral images so as to achieve sufficient dimensionality prior to OSP classification. It is then followed by an unsupervised OSP classifier, called automatic target detection and classification algorithm (ATDCA) for classification. The effectiveness of the proposed GOSP is evaluated by a 3-band SPOT and a 4-band Landsat MSS images. The experimental results has shown that GOSP significantly improves the classification performance of OSP. © 1998 SPIE.

关键词： Landsat

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