As common unsupervised clustering methods, K-means and spectral clustering methods have some disadvantages and limitations in clustering hyperspectral remote sensing image. Aiming at these problems, a new clustering method of hyperspectral image is proposed in this study. In this method, based on the feature reduction dimension of hyperspectral image data, K-means algorithm is first used to make rough clustering of images. Then spectral clustering method is used to cluster the results of coarse clustering with high precision. Compared with K-means clustering algorithm, this method effectively improves the classification accuracy of hyperspectral image clustering. Experiments on simulated data and real hyperspectral data show that this method has good clustering performance compared with K-means and spectral clustering methods.
YANG Suixin
,
GENG Xiurui
,
YANG Weitun
,
ZHAO Yongchao
,
LU Xiaojun
. A method of hyperspectral remote sensing image classification based on spectral clustering[J]. Journal of University of Chinese Academy of Sciences, 2019
, 36(2)
: 267
-274
.
DOI: 10.7523/j.issn.2095-6134.2019.02.015
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