A spectral-spatial kernel-based method for hyperspectral imagery classification

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• 作者：Li Li^a ; ^{lili.jnu@gmail.com} ; ^{liliiot@163.com} ; Hongwei Ge^a ; ^{ghw8601@163.com} ; Jianqiang Gao^b ; ^{dr.jq.gao@gmail.com} ; ^{jianqianggaohh@126.com}
• 关键词：Hyperspectral imagery classification (HIC) ; Area median filtering (AMF) ; Spectral-spatial kernel (SSK) ; Support vector machine (SVM)
• 刊名：Advances in Space Research
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：59
• 期：4
• 页码：954-967
• 全文大小：3017 K
• 卷排序：59

文摘

Spectral-based classification methods have gained increasing attention in hyperspectral imagery classification. Nevertheless, the spectral cannot fully represent the inherent spatial distribution of the imagery. In this paper, a spectral-spatial kernel-based method for hyperspectral imagery classification is proposed. Firstly, the spatial feature was extracted by using area median filtering (AMF). Secondly, the result of the AMF was used to construct spatial feature patch according to different window sizes. Finally, using the kernel technique, the spectral feature and the spatial feature were jointly used for the classification through a support vector machine (SVM) formulation. Therefore, for hyperspectral imagery classification, the proposed method was called spectral-spatial kernel-based support vector machine (SSF-SVM). To evaluate the proposed method, experiments are performed on three hyperspectral images. The experimental results show that an improvement is possible with the proposed technique in most of the real world classification problems.