Hyperspectral image classification based on spatial graph kernel

This paper proposed a new strategy for spectral-spatial hyperspectral image classification. The proposed strategy, has concentrated on spatial graph kernel and automatic “outstanding” spatial structures. Contribution of this paper is related to analysing probabilistic classification results for selecting the most reliable classified pixels as outstanding points of spatial regions. Experimental implementations with four datasets (Indiana Pine, Hekla, University of Pavia and Centre of Pavia) represent advantageous of the proposed method in hyperspectral remote sensing applications. From empirical results, we conclude that the novel proposed approach meaningfuly decreases of oversegmentation, and improves the classification accuracies and provides classification maps with more homogeneous regions.