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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:粗糙小波粒度空间及多光谱遥感图像分类
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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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