Technology of Graphic & Image
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928-931

Hyperspectral image classification based on dimensionality reduction Gabor feature and decision fusion

Yang Xiujie1
Gao Li2
1. School of Digital Media, Chongqing College of Electronic Engineering, Chongqing 401331, China
2. Dept. of Student Work, Southwest University, Chongqing 400715, China

Abstract

Aiming at the problem of ignoring spatial features in traditional hyperspectral image classification algorithm, this paper proposed a hyperspectral image classification algorithm based on dimensionality reduction Gabor feature and decision fusion. Firstly, it intelligently grouped the adjacent and hyper correlated spectral bands by coefficient correlation matrix. Then, it extracted Gabor features in each group from the PCA projection subspace to quantify the local direction and scale features. Then, it reduced the dimensionality of these feature subspaces by preserving the locality of the decomposition of nonnegative matrices. Finally, it classified the reduced dimension features by Gaussian mixture model, and merged the classification results by decision fusion rules. Experimental results show that the proposed algorithm is superior to eight kinds of traditional and existing advanced classification algorithms.

Foundation Support

重庆市教委课题项目(KJ1729408)
重庆市教委教改重点项目(162071)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.09.0670
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 3
Section: Technology of Graphic & Image
Pages: 928-931
Serial Number: 1001-3695(2020)03-064-0928-04

Publish History

[2020-03-05] Printed Article

Cite This Article

杨秀杰, 高丽. 基于降维Gabor特征和决策融合的高光谱图像分类 [J]. 计算机应用研究, 2020, 37 (3): 928-931. (Yang Xiujie, Gao Li. Hyperspectral image classification based on dimensionality reduction Gabor feature and decision fusion [J]. Application Research of Computers, 2020, 37 (3): 928-931. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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