Small Size Class Preserving Classification Based on Segmentation for Hyperspectral Data

Noises in hyper spectral data make it difficult to accurately classify the domains. In order to solve this problem, some filtering methods are proposed; however, filtering processes disturb small size classes and sharp boundaries. To protect the domain boundaries in hyperspectral data classification, we applied normalized cuts segmentation in the preprocessing before classification. The proposed methodologies show higher OA and AA, which means that the domain boundaries are maintained, and classification accuracies of small classes are improved. We also found that the classification accuracies are not sensitive to the number of clusters. The proposed methodology is useful for the combination of smoothing filters that show high classification performances.