GABOR-FILTERING-BASED PROBABILISTIC COLLABORATIVE REPRESENTATION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

被引：0

作者：

Xu, Yan ^{[1
]}

Du, Qian ^{[1
]}

Li, Wei ^{[2
]}

Younan, Nicolas ^{[1
]}

机构：

[1] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39759 USA

[2] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China

来源：

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年

关键词：

Hyperspectral imagery; classification; Gabor filtering; probabilistic; collaborative representation; NEAREST REGULARIZED SUBSPACE;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents Gabor-filtering-based probabilistic collaborative representation for hyperspectral image classification. Compared with the original collaborative representation classifier (CRC) and the CRC using Gabor features, the proposed classifier offers superior classification performance. The regularized versions of CRC using Gabor features have excellent classification performance; however, those classifiers have high computational cost. Experimental results show that the proposed approach can generate high classification accuracy with lower computational cost.

引用

页码：5081 / 5084

页数：4

共 19 条

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