关键词: 高光谱图像, 图像分类, 随机森林, 独立成分分析

Abstract: In order to make full use of spectral information and spatial structure information of hyperspectral image, a new classification approach based on random forest for hyperspectral data classification is proposed. Firstly, the dimension of the data is reduced by principal component analysis. The spectral features are extracted by independent component analysis, which can eliminate the spatial correlation among pixels. Then morphological analysis is used to extract the spatial structure features of pixels. Moreover, random forests are respectively constructed based on spectral information and spatial structure feature, and the spatial continuity constraint is introduced to fix the prediction results of pixels. Finally, the classification result is decided by voting strategy. Experiments on AVIRIS and ROSIS hyperspectral images demonstrate that the proposed approach has better performance than the classical methods and methods based on single feature.

Key words: hyperspectral image, image classification, random forest, independent component analysis