LAUSR

The issue of limited labeled samples is still grave in hyperspectral image classification. Semisupervised learning (SSL) utilizing both labeled and unlabeled samples promotes a solution to this issue. However, it has been found that the performance of a single SSL is frail when the labeled samples are limited. To tackle this problem, we propose 3-D-Gabor and multiple graphs semisupervised framework (3DG-MGSF). The whole framework is tripartite, including multiviews generation and selection, multiple graphs-based label propagation (LP), and double layers classification fusion process. Specifically, a number of 3-D-Gabor filters with various directions and frequencies are employed to generate multiple views. Afterward, a double multiviews selection procedure is applied to ensure the sufficiency and diversity of multiple views. Subsequently, it is time for the multiple graphs-based LP to be put on its pump. Moreover, spatial and spectral classifications are combined based on the weighted re-fusion algorithm to obtain the final classification. Experimental results illustrate numerically and visually the significantly superior performance of the proposed algorithm compared with four state-of-the-art algorithms with few labeled samples.