Spatial–Spectral Information-Based Semisupervised Classification Algorithm for Hyperspectral Imagery

Semisupervised learning has shown its great potential in land cover mapping. It exploits the information of unlabeled training samples and converts those samples to labeled training samples to enhance classification. In this paper, the spatial information extracted by a two-dimensional (2-D) Gabor filter was stacked with spectral information first, and then the spatial neighborhood information of labeled training samples was combined with active learning (AL) algorithm to select the most useful and informative samples, which were used as the unlabeled set to aid the probability model-based supervised support vector machine (SVM). Experiments on two hyperspectral datasets showed that the spatial-spectral information-based semisupervised classification algorithm ($bf{S}^{bf{2}} bf{ISC}$) can produce high classification accuracy with a small number of labeled samples, and outperformed the compared semisupervised algorithms.