Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 54, Issue 12, Pages 7066-7076Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2016.2594848
Keywords
Collaborative graph; dimensionality reduction; graph embedding; hyperspectral data; Laplacian matrix
Categories
Funding
- National Natural Science Foundation of China [61571033, 61302164]
- Fundamental Research Funds for the Central Universities [BUCTRC201401, BUCTRC201615, XK1521]
Ask authors/readers for more resources
Collaborative graph-based discriminant analysis (CGDA) has been recently proposed for dimensionality reduction and classification of hyperspectral imagery, offering superior performance. In CGDA, a graph is constructed by l(2)-norm minimization-based representation using available labeled samples. Different from sparse graph-based discriminant analysis (SGDA) where a graph is built by l(1)-norm minimization, CGDA benefits from within-class sample collaboration and computational efficiency. However, CGDA does not consider data manifold structure reflecting geometric information. To improve CGDA in this regard, a Laplacian regularized CGDA (LapCGDA) framework is proposed, where a Laplacian graph of data manifold is incorporated into the CGDA. By taking advantage of the graph regularizer, the proposed method not only can offer collaborative representation but also can exploit the intrinsic geometric information. Moreover, both CGDA and LapCGDA are extended into kernel versions to further improve the performance. Experimental results on several different multiple-class hyperspectral classification tasks demonstrate the effectiveness of the proposed LapCGDA.
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