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1.发明授权公开(公告)号：US10509984B2
公开(公告)日：2019-12-17
申请号：US15978189
申请日：2018-05-13
申请人： SHENZHEN UNIVERSITY
摘要： The present invention provides a method for extracting and classifying features of hyperspectral remote sensing image, including: an sampling step, a binarizing step, a coding step, a statistical calculating step, a concatenating step, and a classifying step. The present invention further provides a system for extracting and classifying features of hyperspectral remote sensing image. The technical solution provided by the present invention can make full use of the contextual relationship between the spectral domain and the spatial domain in a hyperspectral remote sensing image by extending two-dimensional LBPs into three-dimensional LBPs, and has good robustness to noise by introducing a relaxation threshold discrimination operation. Furthermore, the rotation-invariant three-dimensional LBP model provided by the present invention takes account of the essential characteristics of the hyperspectral remote sensing image, and therefore the present solution has advantages that it is targeted, simple in operation and high in calculation efficiency.
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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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3.发明申请公开(公告)号：US20180260657A1
公开(公告)日：2018-09-13
申请号：US15978189
申请日：2018-05-13
申请人： SHENZHEN UNIVERSITY
摘要： The present invention provides a method for extracting and classifying features of hyperspectral remote sensing image, including: an sampling step, a binarizing step, a coding step, a statistical calculating step, a concatenating step, and a classifying step. The present invention further provides a system for extracting and classifying features of hyperspectral remote sensing image. The technical solution provided by the present invention can make full use of the contextual relationship between the spectral domain and the spatial domain in a hyperspectral remote sensing image by extending two-dimensional LBPs into three-dimensional LBPs, and has good robustness to noise by introducing a relaxation threshold discrimination operation. Furthermore, the rotation-invariant three-dimensional LBP model provided by the present invention takes account of the essential characteristics of the hyperspectral remote sensing image, and therefore the present solution has advantages that it is targeted, simple in operation and high in calculation efficiency.
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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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