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

Authors:
Yen-Wei Chen;Xian-Hua Han
Affiliations:
Elect & Inf. Eng. School, Central South Univ. of Forest and Tech., Changsha, China and Graduate School of Science and Engineering, Ritsumeikan University, Japan;Elect & Inf. Eng. School, Central South Univ. of Forest and Tech., Changsha, China and Graduate School of Science and Engineering, Ritsumeikan University, Japan
Venue:
Computational Color Imaging
Year:
2009

Citing 0
Cited 1

SOR based fuzzy k-means clustering algorithm for classification of remotely sensed images

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

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.