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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

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A unified unsupervised clustering algorithm and its first application to landcover classification

A unified unsupervised clustering algorithm and its first ap...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Y. Yu I. Bloch A. Trouve Département TSI CNRS URA 820 Ecole Nationale Supérieure des TéIécommunications Paris France LAGA CNRS UMR 7539 Institut Galilee Université Paris 13 Villetaneuse France

The problem of classification is so fundamental that it has been intensively investigated by many researchers from different domains. In this paper, we present a novel unsupervised clustering algorithm derived from the techniques of probabilistic modeling which is implemented by a stochastic gradient algorithm. Then its application to challenging landcover classification based on Daedalus data of the SMART project is explored by combining both spectral feature and spatial contextual information. Our first experiments show its potential usefulness in remote sensing.

关键词： Clustering algorithms Classification algorithms Clustering methods Optimization methods remote sensing pattern recognition Statistics Psychology Information retrieval image retrieval

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Spectral fuzzy classification system for target recognition

Spectral fuzzy classification system for target recognition

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conference of the North American Fuzzy Information processing Society - NAFIPS

作者： A. Del Amo D. Gomez J. Montero Electronics Systems-Grand Rapids Smiths Aerospace MI USA Department Statistics and O R. Complutense University of Madrid Madrid Spain

The goal of this paper is to present an algorithm for terrain matching, leveraging an existing fuzzy clustering algorithm, and modifying it to its supervised version, in order to apply the algorithm to georegistration and, later on pattern recognition. Georegistration is the process of adjusting one drawing or image to the geographic location of a "known good" reference drawing, image, surface or map, The georegistration problem can be treated as a pattern recognition problem; and it can be applied to the target detection problem. The terrain matching algorithm will be based on fuzzy set theory as a very accurate method to represent the imprecision of the real world, and presented as a multicriteria decision making problem. The energy emitted and reflected by the Earth's surface has to be recorded by relatively complex remote sensing devices that have spatial, spectral and geometrical resolution constraints. Errors usually slip into the data acquisition process. Therefore, it is necessary to preprocess the remotely sensed data, prior to analyzing it (image restoration, involving the correction of distortion, degradation and noise introduced during the rendering process). In this paper we shall assume that all these problems have been solved, focusing our study on the image classification of a corrected image being close enough, both geometrically and radiometrically, to the radiant energy characteristics of the target scene. In particular, at a first stage we consider each pixel individually; and a class will be assigned to each pixel, taking into account several values measured in separate spectral bands. Then we shall describe an automatic detection system based on a previous algorithm developed in A. Del Amo et al., introducing now the fuzzy partition model proposed by A. Del Amo et al. A first phase will lead to a spectral definition of patterns; and a second phase will lead to classification and recognition. Similarity measures will then allow us to evaluate th

关键词： Fuzzy systems Target recognition Clustering algorithms pattern recognition pattern matching Surface treatment Object detection Fuzzy set theory Decision making Earth

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A fusion algorithm of remote sensing image based on discrete wavelet packet

A fusion algorithm of remote sensing image based on discrete...

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International conference on Machine Learning and Cybernetics (ICMLC)

作者： Hai-Hui Wang Jia-Xiong Peng Wei Wu State Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780378652

image fusion refers to the techniques that integrate complementary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and the compute processing tasks. In this paper, a new image fusion algorithm based on wavelet packet transform to fuse multisensor images is presented. Discrete wavelet transform (DWT) can offer a more precise way for image analysis, than other multi-resolution analysis. It decomposes an image into low frequency band and high frequency band in different level, and it can also be reconstructed gradually in different level. But this method only decomposes low frequency band in a higher scale, so that it omits some useful details of the images. In this paper, we present a new image fusion algorithm. In the algorithm, we use discrete wavelet packet transform (DWPT) to decompose and reconstruct the images. When images are merged in wavelet packet space, different frequency ranges are processed differently. It can merge information from original images adequately and improve abilities of information analysis and feature extraction. This image fusion is performed at the pixel level. In this fusion algorithm, a feature-based fusion rule is used to combine original subimages and to form a pyramid for the fused image. Through merging remote sensing images from multi-sensor to a same object by applying method of wavelet packet analysis, we have obtained a fused picture. In this paper, mutual information is employed as a means of objective assessing image fusion performance. The experiment results show that this fusion algorithm, based on wavelet packet transform, is an effective approach in image fusion area.

关键词： remote sensing Discrete wavelet transforms Wavelet packets image fusion Frequency image analysis Wavelet analysis image reconstruction image sensors Humans

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Integer Haar wavelet for romote sensing image compression 6

Integer Haar wavelet for romote sensing image compression

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6th International conference on Signal processing, ICSP 2002

作者： Zhenhua, Wang Hongbo, Xu Yan, Tian Jinwen, Tian Jian, Liu State Key Lab. for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)0780374886

With the development of remote sensing technique, onboard data compression has become an urgent need and a lot of study has been directed toward the development of efficient techniques. In this paper, the construction approach of integer Haar is discussed briefly, and a'simple image compression scheme based on integer Haar Wavelet Transform and block DPCM is proposed. The scheme can be easily designed for data processing in real-time system of remote sensing with parellel algorithm. Simulation experimental results demonstrate that the proposed approach is a efficient image compression method. © 2002 IEEE.

关键词： image compression

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Unsupervised data fusion for hyperspectral imaging

Unsupervised data fusion for hyperspectral imaging

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conference on image and Signal processing for remote sensing Vii

作者： Jiménez, LO Velez-Reyes, M Rivera, JL Velázquez, HT Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Dept Elect & Comp Engn Mayaguez PR 00681 USA

ISBN: (纸本)0819442666

Hyperspectral images contain a great amount of information in terms of hundreds of narrowband channels. This should lead to better parameter estimation and to more accurate classifications. However, traditional classification methods based on multispectral analysis fail to work properly on this type of data. High dimensional space poses a difficulty in obtaining accurate parameter estimates and as a consequence this makes unsupervised classification a challenge that requires new techniques. Thus, alternative methods are needed to take advantage of the information provided by the hyperdimensional data. Data fusion is an alternative when dealing with such large data sets in order to improve classification accuracy. Data fusion is an important process in the areas of environmental systems, surveillance, automation, medical imaging, and robotics. The uses of this technique in remote sensing have been recently expanding. A relevant application is to adapt the data fusion approaches to be used on hyperspectral imagery taking into consideration the special characteristics of such data. The approach of this paper is to presents a scheme that integrates information from most of the hyperspectral narrow-bands in order to increase the discrimination accuracy in unsupervised classification.

关键词： remote sensing pattern recognition hyperspectral data high dimensional data clustering unsupervised classification data fusion decision fusion band subset selection dimension reduction

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Natural and artificial target recognition by hyperspectral remote sensing data

Natural and artificial target recognition by hyperspectral r...

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7th Battlespace Digitization and Network-Centric Warfare conference

作者： Zhang, B Liu, LY Zhao, YC Xu, GX Zheng, LF Tong, QX Chinese Acad Sci Inst Remote Sensing Applicat Beijing 100101 Chaoyang Dist Peoples R China

ISBN: (纸本)081944491X

Recent advances in remote sensing have led the way for the development of hyperspectral sensors and the applications of the hyperspectral data. Hyperspectral remote sensing is a relatively new technology, which is currently being investigated by researchers and scientists with regard to the detection and identification of minerals, terrestrial vegetation, and man-made materials and backgrounds. The airborne hyperspectral imaging data have operationally been used to a number of land-use, natural environment, geology, agriculture and other studies. In this study, airborne hyperspectral imaging data were tested in vegetation and man-made object identification. Natural grassland and artificial grassland, different types of crops, different types of forest and bush, different types of metal slabs in construction project have been precisely classified and greatly identified. In these works, the Operational Modular Imaging Spectrometer (ONUS) provides the imaging spectrometer data. OMIS has 128 spectral bands, including visible, short wave infrared, middle infrared and thermal infrared spectral region. Results suggest that hyperspectral imaging data, especially with short wave infrared and thermal infrared wavelength, have broad application perspectives in object identification.

关键词： hyperspectral remote sensing image processing target recognition

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Entropy optimized contrast stretch to enhance remote sensing imagery

Entropy optimized contrast stretch to enhance remote sensing...

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16th International conference on pattern recognition, ICPR 2002

作者： Xu, Xiaoyin Miller, Eric L. Center for Subsurface Sensing and Imaging Systems Dept. of ECE Northeastern University BostonMA02115-5000 United States

ISBN: (纸本)0769516963

This paper presents a contrast stretch (CS) method based on minimum entropy constraint to enhance images obtained in remote sensing applications such as ground penetrating radar (GPR), synthetic aperture radar, and infra-red imagery. The CS enhances contrast of the low-contrast part of an image. In remote sensing, it is usually the desirable signals that are of low contrast while interference of high contrast. The CS modifies the original image such that pixel values above and below preset boundaries are set to zero and the maximum possible pixel value and the pixel values failing between the boundaries are stretched out to enhance the contrast of the image. Using the CS we can enhance the contrast of desirable signals from, for example, a buried landmine or an object obscured by some interference. On the other hand, the CS inevitably enhances other parts of a remote sensing images, such as clutters and measurement noise. Therefore there is a trade-off in using the CS. It is beneficial to find the correct "cut-off" boundaries in the CS in some optimal sense. We propose using minimum entropy as a criterion of looking for the optimal CS parameter. Using field data from GPR application, we show that improved image can be obtained which makes further processing such as detection more accurate. © 2002 IEEE.

关键词： Synthetic aperture radar

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Automated identification of endmembers from hyperspectral data using mathematical morphology

Automated identification of endmembers from hyperspectral da...

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conference on image and Signal processing for remote sensing Vii

作者： Plaza, A Martínez, P Gualtieri, JA Pérez, RM Univ Extremadura Dept Informat GRNPS Badajoz Spain

ISBN: (纸本)0819442666

One of the most widely used approaches to analyze hyperspectral data is pixel unmixing, which relies on the identification of the purest spectra from the data cube. Once these elements, known as "endmembers", are extracted, several methods can be used to map their spatial distributions, associations and abundances. A large variety of methodologies have been recently proposed with the purpose of extracting endmembers from hyperspectral data. Nevertheless, most of them only rely on the spectral response;spatial information has not been fully exploited yet, specially in unsupervised classification. The integration of both spatial and spectral information is becoming more relevant as the sensors tend to increase their spatial/spectral resolution. Mathematical morphology is a non-linear image analysis and pattern recognition technique that has proved to be especially well suited to segment images with irregular and complex shapes, but has rarely been applied to the classification/segmentation of multivariate remote sensing data. In this paper we propose a completely automated method, based on mathematical morphology, which allows us to integrate spectral and spatial information in the analysis of hyperspectral images. The accuracy of the proposed algorithm is tested by its application to real hyperspectral data, and the results provided are compared to those found using other existing endmember extraction algorithms.

关键词： remote sensing hyperspectral data spatial/spectral analysis endmember extraction image processing

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Entropy optimized contrast stretch to enhance remote sensing imagery

Entropy optimized contrast stretch to enhance remote sensing...

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作者： Xu, Xiaoyin Miller, Eric L. Center for Subsurface Sensing and Imaging Systems Dept. of ECE Northeastern University Boston MA 02115-5000 United States

This paper presents a contrast stretch (CS) method based on minimum entropy constraint to enhance images obtained in remote sensing applications such as ground penetrating radar (GPR), synthetic aperture radar, and infra-red imagery. The CS enhances contrast of the low-contrast part of an image. In remote sensing, it is usually the desirable signals that are of low contrast while interference of high contrast. The CS modifies the original image such that pixel values above and below preset boundaries are set to zero and the maximum possible pixel value and the pixel values falling between the boundaries are stretched out to enhance the contrast of the image. Using the CS we can enhance the contrast of desirable signals from, for example, a buried landmine or an object obscured by some interference. On the other hand, the CS inevitably enhances other parts of a remote sensing images, such as clutters and measurement noise. Therefore there is a trade-off in using the CS. It is beneficial to find the correct "cut-off" boundaries in the CS in some optimal sense. We propose using minimum entropy as a criterion of looking for the optimal CS parameter. Using field data from GPR application, we show that improved image can be obtained which makes further processing such as detection more accurate. © 2002 IEEE.

关键词： image enhancement

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Thresholding video images for text detection

Thresholding video images for text detection

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16th International conference on pattern recognition, ICPR 2002

作者： Du, Eliza Yingzi Chang, Chein-I Thouin, Paul D. 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 Department of Defense Fort MeadeMD20755 United States

ISBN: (纸本)0769516963

Thresholdging video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. As a result, thresholding methods that work well for document images may not work effectively for video images in some applications. This paper investigates the issue of thresholding video images for text detection and further develops a relative entropy-based thresholding approach that can effectively extract text from complicated video images. In order to demonstrate its performance a comparative study is conducted among the proposed thresholding method and several thresholding techniques which are widely used for document and gray scale images. The experimental results show that thresholdging video images is far more difficult than thresholding document images and simple histogrambased methods generally do not perform well. © 2002 IEEE.

关键词： pattern recognition

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