Supervised Local Subspace Learning for Region Segmentation and Categorization in High-Resolution Satellite Images
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.
Keywordssupervised locality preserving projections region segmentation categorization high-resolution satellite images subspace learning independent component analysis generalization
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