关键词: 高光谱图像分类, 多视图学习, 主动学习, 查询策略, 三维Gabor

Abstract: The acquisition of labeled samples is a difficult issue on hyperspectral remote sensing images classification. The multi-view active learning scheme how to query the most informative samples is carried out. Firstly the 3D Gabor filters with different scales and directions are adopted to extract the spectral and spatial features simultaneously. Then, the obtained 3D Gabor features with stronger discriminant ability are selected to construct multiple views. Finally, a new query strategy based on the Minimum Posteriori Probability Difference（MPPD） for multi-view active learning is proposed. Experiments are conducted on two benchmark hyperspectral image datasets, including ROSIS Pavia University and AVIRIS Indiana Pines, by using the proposed multi-view active learning framework and the MPPD query strategy. In the initial step, 30 labeled samples are used to learn the classifiers, and after 100 iterations, the overall accuracies of Pavia and Indiana datasets reach 94.16% and 91.30% respectively, which shows that the 3D Gabor filters can effectively extract the spectral and spatial features, and generate multiple views with diversity and complementarity. At the same time, the MPPD strategy can query the most valuable samples.

Key words: hyperspectral image classification, multi-view learning, active learning, query strategy, 3D Gabor