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

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Spatial data mining: clustering of hot spots and pattern recognition

Spatial data mining: clustering of hot spots and pattern rec...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Seng Chuan Tay Wynne Hsu Kim Hwa Lim Lee Chen Yap Centre for Remote Imaging Sensing and Processing National University of Singapore Singapore Department of Computer Science National University of Singapore Singapore

ISBN: (纸本)0780379292

Spatial data mining is the extraction of implicit knowledge, spatial relations or other patterns not explicitly stored in spatial database. The focus of this paper is placed on the information derivation of spatial data. Geographical coordinates of hot spots in forest fire regions, which are extracted from the satellite images, are studied and used in the detection of likely fire points. False alarms can occur in the derived hotspots. While this false information can be identified by comparing the radiance detected at several bands, we introduce a different approach to remove some of the false alarms. We use clustering and a Hough transformation to determine regular patterns in the derived hotspots and classify them as false alarms on the assumption that fires usually do not spread in regular patterns such as in a straight line. This project demonstrates the application of spatial data mining to reduce false alarms from the set of hotspots derived from NOAA images.

关键词： pattern recognition Fires Data mining Satellites remote monitoring Computer science image databases Spatial databases Focusing

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Simulation of interferogram image for spaceborne SAR system

Simulation of interferogram image for spaceborne SAR system

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Kun Ren V. Prinet Xiangquan Shi Gang Wu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Department of Electronic Engineering Nanjing University of Science and Technology of PLA Nanjing China

In interferometric synthetic aperture radar (InSAR) processing, simulation of interferogram is a common practice. It is used as synthetic data to test and validate the whole chain of InSAR processing from the interferogram creation to the DEM reconstruction. Simulators for interferometry, those have been reported in the literature, are developed in very simplified imagery geometry model. In this paper, a interferogram simulator which deal with real radar sensor parameters and orbit data is presented.

关键词： Synthetic aperture radar interferometry Spaceborne radar Earth Synthetic aperture radar Solid modeling Geometry Satellites Testing Radar imaging Space technology

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An unsupervised approach to color video thresholding

An unsupervised approach to color video thresholding

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

作者： Eliza Yingzi Du Chein-I Chang Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD

ISBN: (纸本)0780376633

Thresholding color video images is challenging because of the low spatial resolution and the complex backgrounds. This paper investigates the issue of thresholding these images by reducing the number of colors in order to improve automated text detection and recognition. An unsupervised thresholding approach is presented which reduces the background complexity while retaining the important text character pixels. The experiments show that our proposed thresholding approach performs significantly better than simple image histogram-based methods which generally do not produce satisfactory results.

关键词： Color image segmentation Information retrieval image retrieval image resolution Fuses Gray-scale Pixel remote sensing Signal processing

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Band selection using independent component analysis for hyperspectral image processing

Band selection using independent component analysis for hype...

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Applied imagery pattern recognition Workshop (AIPR)

作者： Hongtao Du Hairong Qi Xiaoling Wang R. Ramanath W.E. Snyder Dept. of Elec. and Comp. Engineering University of Tennessee Knoxville TN USA Dept. of Elec. and Comp. Engineering University of Tennessee Knoxville TN Dept. of Elec. and Comp. Engineering North Carolina State University Raleigh NC USA

Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Independent Component Analysis (ICA). This method, instead of transforming the original hyperspectral images, evaluates the weight matrix to observe how each band contributes to the ICA unmixing procedure. It compares the average absolute weight coefficients of individual spectral bands and selects bands that contain more information. As a significant benefit, the ICA-based band selection retains most physical features of the spectral profiles given only the observations of hyperspectral images. We compare this method with ICA transformation and Principal Component Analysis (PCA) transformation on classification accuracy. The experimental results show that ICA-based band selection is more effective in dimensionality reduction for HSI analysis.

关键词： Independent component analysis Hyperspectral imaging image processing Hyperspectral sensors Principal component analysis Feature extraction Matrix decomposition Military computing image analysis remote sensing

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pattern recognition methods as tools of restoration project in Maavesi water area

Pattern recognition methods as tools of restoration project ...

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IEEE International Geoscience and remote sensing Symposium (IGARSS 2002)/24th Canadian Symposium on remote sensing

作者： Kaarna, A Spacil, R Jantunen, M Lappeenranta Univ Technol Dept Informat Technol FIN-53851 Lappeenranta Finland

ISBN: (纸本)078037536X

In this report we examine the usability of different computational image segmentation and recognition methods as a tool of water area restoration projects. An airborne, mosaic image of Maavesi water area in South-east Finland was measured in September 2000 with a three-channel camera covering parts from the spectrum in red, green and infra-red wavelengths. image segmentation and recognition methods were then applied in purpose to recognise and localize the aquatic macrophyte plant communities into several groups or by species. Results were confirmed by a field study performed in September - October 2001. The main interest of image processing was to have data for design and monitoring of water plant biomass removal in near future.

关键词： remote sensing

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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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image classification in complex spaces

Image classification in complex spaces

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IEEE International Geoscience and remote sensing Symposium (IGARSS 2002)/24th Canadian Symposium on remote sensing

作者： Sadjadi, F Lockheed Martin Corp St Anthony MN 55418 USA

ISBN: (纸本)078037536X

This paper addresses issues related to classification of images in complex spaces. The image is represented in terms of a phase and amplitude components. The classifier optimizes functions of joint real and imaginary conditional probability density functions. Bound on the total probability of errors in terms of Rayliegh quotient is derived and compared to the cases where non-complex amplitude-only signal is used. Examples of application of the proposed approach on polarimetric radar imagery indicate several orders of magnitude improvement in performance.

关键词： Bayes classifiers Fisher distance image classification pattern recognition Rayleigh quotient radar processing signal processing

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Proceedings of SPIE: image and Signal processing for remote sensing VIII

Proceedings of SPIE: Image and Signal Processing for Remote ...

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image and Signal processing for remote sensing VII

This Volume 4885 of the conference proceedings contains 51 papers. Topics discussed include image and signal processing for remote sensing, image registration and calibration, resolution improvement and restoration, image segmentation and object recognition, image analysis and parameter estimation, structural analysis and object recognition, transform methods and statistical classification, analysis of radar signals and images, hyperspectral image coding, hyperspectral image processing and analysis and classification of hyperspectral images.

关键词： remote sensing

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Misclassification correction in primary local recognition of component images of multichannel remote sensing data

Misclassification correction in primary local recognition of...

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conference on image and Signal processing for remote sensing VII

作者： Niemistö, AJ Lukin, VV Dolia, AN Yli-Harja, OP Shmulevich, I Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)0819442666

We propose an automatic classification procedure for multichannel remote sensing data. The method consists of several stages. An important stage is the correction of misclassifications based on the use of a nonlinear graph-based estimation technique recently introduced by us. The misclassification correction method is optimized by means of a training-based framework using genetic algorithms. It is shown that this provides a considerable improvement in classification accuracy. After primary local recognition and misclassification correction of all component images, an approach to further use the obtained data is considered. At this joint classification stage we introduce novel subclasses like "common homogeneous region", "common edge", "small sized object in one or two components", etc. Numerical simulation data as well as real image processing results are presented to confirm the basic steps of remote sensing data classification and the efficiency of the proposed approach.

关键词： remote sensing image recognition misclassification correction multichannel images training-based optimization genetic algorithms

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