Spectral–Spatial Hyperspectral Image Classification Based on Multiple Views and Multigraphs Fusion With Few Labeled Samples

Abstract

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 single SSL is frail when the labeled samples is limited. To tackle this problem, we propose 3D-Gabor and multiple graphs semisupervised framework (3DG-MGSF). The whole framework is tripartite, including multi-views generation and selection, multiple graphs-based label propagation, and double layers classification fusion process. Specifically, a number of 3D-Gabor filters with various directions and frequencies are employed to generate multiple views. Afterwards, a double multi-views selection procedure is applied to ensure the sufficiency and diversity of multiple views. Subsequently, it is time for the multiple graphs-based label propagation to 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 proposed algorithm compared with four state-of-the-art algorithms with few labeled samples.