title: Spatial-spectral method for classification of hyperspectral images.
abstract: Spatial-spectral approach with spatially adaptive classification of hyperspectral images is proposed. The rotation-invariant spatial texture information for each object is exploited and incorporated into the classifier by using the modified local Gabor binary pattern to distinguish different types of classes of interest. The proposed method can effectively suppress anisotropic texture in spatially separate classes as well as improve the discrimination among classes. Moreover, it becomes more robust with the within-class variation. Experimental results on the classification of three real hyperspectral remote sensing images demonstrate the effectiveness of the proposed approach.
authors.affiliation: Science and Technology on Multi-spectral Information Processing Laboratory, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei, China. xyjconf10@163.com
authors.forename: Xiaoyong
authors.initials: X
authors.lastname: Bian
authors.forename: Tianxu
authors.initials: T
authors.lastname: Zhang
authors.forename: Luxin
authors.initials: L
authors.lastname: Yan
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Zhang
authors.forename: Houzhang
authors.initials: H
authors.lastname: Fang
authors.forename: Hai
authors.initials: H
authors.lastname: Liu