Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 8, Issue 2, Pages 369-373Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2010.2076407
Keywords
Dimensionality reduction; hyperspectral image classification; pairwise constraints; sparse representation
Categories
Funding
- National Natural Science Foundation of China [60875030]
Ask authors/readers for more resources
Dimensionality reduction is an important task in the analysis of hyperspectral image data. While traditional dimensionality reduction methods use class labels as prior information, this letter presents a general semisupervised dimensionality reduction framework for hyperspectral image classification based on new prior information, i.e., pairwise constraints which specify whether a pair of examples belongs to the same class or not. The proposed semisupervised dimensionality reduction framework contains two terms: 1) a discrimination term that assesses the separability between classes; and 2) a regularization term that characterizes some property of the original data set. Furthermore, a novel semisupervised dimensionality reduction method is derived from the framework based on sparse representation. Experimental results on two hyperspectral image data sets show that the proposed algorithms are remarkably effective in comparison to traditional dimensionality reduction methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available