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公开(公告)号：US10783371B2
公开(公告)日：2020-09-22
申请号：US15980701
申请日：2018-05-15
申请人： SHENZHEN UNIVERSITY
发明人： Sen Jia , Jie Hu , Yao Xie , Linlin Shen
摘要： The present invention provides a Gabor cube feature selection-based classification method for hyperspectral remote sensing images, comprising the following steps: generating three-dimensional Gabor filters according to set frequency and direction parameter values; convoluting hyperspectral remote sensing images with the three-dimensional Gabor filters to obtain three-dimensional Gabor features; selecting three-dimensional Gabor features, classification contribution degrees to various classes of which meet preset requirements, from the three-dimensional Gabor features; and classifying the hyperspectral remote sensing images by a multi-task joint sparse representation-based classification means by using the selected three-dimensional Gabor features. The present invention is based on the three-dimensional Gabor features, and the used three-dimensional Gabor features contain rich local change information of a signal and are competent in feature characterizing. Using a Fisher discriminant criterion not only makes full use of high-level semantics hidden among the features, but also eliminates redundant information and reduces the classification time complexity.
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2.发明申请公开(公告)号：US20180268195A1
公开(公告)日：2018-09-20
申请号：US15980701
申请日：2018-05-15
申请人： SHENZHEN UNIVERSITY
发明人： Sen Jia , Jie Hu , Yao Xie , Linlin Shen
CPC分类号： G06K9/0063 , G06F17/18 , G06K9/00201 , G06K9/46 , G06K9/4619 , G06K9/6228 , G06K9/6234 , G06K9/6267 , G06K2009/00644 , G06K2009/4657
摘要： The present invention provides a Gabor cube feature selection-based classification method for hyperspectral remote sensing images, comprising the following steps: generating three-dimensional Gabor filters according to set frequency and direction parameter values; convoluting hyperspectral remote sensing images with the three-dimensional Gabor filters to obtain three-dimensional Gabor features; selecting three-dimensional Gabor features, classification contribution degrees to various classes of which meet preset requirements, from the three-dimensional Gabor features; and classifying the hyperspectral remote sensing images by a multi-task joint sparse representation-based classification means by using the selected three-dimensional Gabor features. The present invention is based on the three-dimensional Gabor features, and the used three-dimensional Gabor features contain rich local change information of a signal and are competent in feature characterizing. Using a Fisher discriminant criterion not only makes full use of high-level semantics hidden among the features, but also eliminates redundant information and reduces the classification time complexity.
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