Feature extraction and classification of hyperspectral remote sensing image oriented to easy mixed-classified objects

来源期刊：中国有色金属学报(英文版)2005年第z1期

论文作者：ZHANG Lian-peng LIU Guo-lin JIANG Tao

文章页码：160 - 163

Key words：hyperspectral remote sensing； feature extraction； classification

Abstract: The classification of hyperspectral remote sensing data is an important problem theoretically and practically. With the increase of spectral bands, the separability of objects on remote sensing image should be improved. But the effects of traditional algorithm on feature extraction such as principal component analysis(PCA) is not so good for hyperspectral image. The key problem is that PCA can only represent the linear structure of data set; while the data clouds of different objects on hyperspectral image usually distribute on a nonlinear manifold. This paper established an algorithm of nonlinear feature extraction named as nonlinear principal poly lines, based on the algorithm, a classifier is constructed and the classification accuracy of hyperspectral image can be improved.