关键词: 高光谱图像分类, 迁移学习, 领域自适应, 图像分割, 同质区

Abstract: As a classic algorithm, Transfer Component Analysis（TCA） has been applied in many different fields. However, due to the large matrix operations involved, TCA is not directly applied to the classification of remote sensing images. In this study, the traditional homogenization region information is used to improve the traditional transfer component analysis idea. A randomly selected point represents a target domain homogenous region to realize one process oftransfer component analysis and classification, and using the majority results of multiple classification as the common class of target domain homogenous region. Experiments on two sets of hyperspectral images demonstrate the effectiveness of the new method.

Key words: hyperspectral image classification, transfer learning, domain adaptation, image segmentation, homogenous region