Hyperspectral image classification by combining empirical mode decomposition with Gabor filtering

被引：0

作者：

Wang L. ^{[1
]}

Wan Y. ^{[1
]}

Lu T. ^{[1
]}

Yang Y. ^{[1
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2016年 / 37卷 / 02期

关键词：

Empirical mode decomposition; Gabor filter; Hyperspectral image; Image classification; Spatial information;

D O I：

10.11990/jheu.201411032

中图分类号：

学科分类号：

摘要：

Considering the shortcomings of traditional texture extraction methods implemented in original data space, in this paper we use the empirical mode decomposition theory to extract the intrinsic mode components of a distinct spatial structure from a hyperspectral image, and perform Gabor filtering on these extracted components. We transferred the traditional texture extraction method to the transform domain. In this way, we propose using a high-precision texture extraction algorithm for decomposing and integrating spatial information based on two-dimensional empirical mode decomposition. We carried out simulation tests on two datasets, and the results show that the improved algorithm effectively improves the classification accuracy of hyperspectral images and has good noise suppression performance. The proposed algorithm is thus clearly superior to the traditional Gabor-PCA algorithm, and can mine hyperspectral image spatial information to a greater extent. © 2016, Editorial Board of Journal of Harbin Engineering. All right reserved.

引用

页码：284 / 290

页数：6

共 13 条

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