Hyperspectral Image Classification Based on Local Gaussian Mixture Feature Extraction

被引：0
|
作者：
Li D. [1 ,2 ]
Kong F. [2 ]
Zhu D. [1 ,2 ]
机构：
[1] Key Laboratory of Space Photoelectric Detection and Perception, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Guangxue Xuebao/Acta Optica Sinica | 2021年 / 41卷 / 06期
关键词：
Classification; Feature extraction; Gaussian mixture model; Hyperspectral image; Image processing;
D O I：
10.3788/AOS202141.0610001
中图分类号：
学科分类号：
摘要：
In order to further improve the classification accuracy of hyperspectral images, a classification method based on local Gaussian mixture feature extraction (LGMFEC) is proposed. The LGMFEC method first constructs a local neighborhood set for each sample based on the spatial structure of the hyperspectral image, and then extracts Gaussian mixture features from the local neighborhood set to fully characterize the spatial-spectral information and the related change information between them, and finally the local Gaussian mixture features are integrated into a support vector machine (SVM) classifier containing a Riemann kernel function to complete the classification task. The experimental results of three sets of general hyperspectral datasets show that the classification performance of the LGMFEC method is better than several advanced classification methods to a large extent, especially when there are fewer training samples. © 2021, Chinese Lasers Press. All right reserved.
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