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ROUGH WAVELET GRANULAR SPACE AND CLASSIFICATION OF MULTISPECTRAL REMOTE SENSING IMAGE
ROUGH WAVELET GRANULAR SPACE AND CLASSIFICATION OF MULTISPECTRAL REMOTE SENSING IMAGE
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机译:粗糙小波粒度空间和多光谱遥感图像的分类
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摘要
Shift-invariant wavelet transform with properly selected wavelet base and decomposition level(s), is used to characterize rough-wavelet granules producing wavelet granulation of a feature space for a multispectral image such as a remote sensing image. Through the use of the granulated feature space contextual information in time and/or frequency domains are analyzed individually or in combination. Neighborhood rough sets (NRS) are employed in the selection of a subset of granulated features that further explore the local and/or contextual information from neighbor granules.
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