Supervised local subspace learning for region segmentation and categorization in high-resolution satellite images

Abstract

We proposed a new feature extraction method based on supervised locality preserving projections (SLPP) for region segmentation and categorization in high-resolution satellite images. Compared with other subspace methods such as PCA and ICA, SLPP can preserve local geometric structure of data and enhance within-class local information. The generalization of the proposed SLPP based method is discussed in this paper. © 2009 Springer Berlin Heidelberg.