Supervised Local Subspace Learning for Region Segmentation and Categorization in High-Resolution Satellite Images

摘要

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.