小波核局部Fisher判别分析的高光谱遥感影像特征提取

doi:10.16451/j.cnki.issn1003-6059.201907006

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Abstract

To improve classification accuracies of hyperspectral remote sensing images and make full use of local information, a feature extraction method for hyperspectral remote sensing images based on local Fisher discriminant analysis with wavelet kernel is proposed. Wavelet kernel function is introduced to map data from a low dimensional space to a high dimensional feature space, and a weighted matrix is employed to calculate scatter matrices. Local Fisher discriminant criterion function is solved to obtain the optimal feature matrix and a better separation in high-dimensional feature space. Experimental results on two open hyperspectral datasets show that the overall classification accuracy and Kappa coefficient of the proposed method are improved compared with other methods.

Key words： Hyperspectral Image Classification Wavelet Kernel Function Local Fisher Discriminant Analysis Feature Extraction

Received: 21 March 2019

ZTFLH:

TP 751

Fund:

Supported by National Natural Science Foundation of China(No.41871379,61540056), Natural Science Foundation of Liaoning Province(No.20180550450), Key Project of Education Department of Liaoning(No.LJ2017ZL003)

About author:: ZHANG Hui, Ph.D. candidate. His research interests include hyperspectral data processing.LIU Wanjun, master, professor. His research interests include digital image proce-ssing.Lü Huanhuan(Corresponding author), Ph.D., associate professor. Her research interests include hyperspectral data processing.

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Cite this article:

ZHANG Hui,LIU Wanjun,Lü Huanhuan. Feature Extraction for Hyperspectral Remote Sensing Image Based on Local Fisher Discriminant Analysis with Wavelet Kernel[J]. , 2019, 32(7): 624-632.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201907006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I7/624

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