Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 11, Issue 3, Pages 651-655Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2013.2273792
Keywords
Hyperspectral image classification (HIC); semi-supervised classification; spatial constraint; spatio-spectral Laplacian support vector machine (SS-LapSVM)
Categories
Funding
- National Basic Research Program of China (973 Program) [2013CB329402]
- National Natural Science Foundation of China [61072108, 61072106, 61271290, 51207002, 61173090, 61272282]
- National Research Foundation for the Doctoral Program of Higher Education of China [9140A24070412DZ0101, 20120203110005, 20110203110006]
- Program for New Century Excellent Talents in University [NCET-10-0668]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT1170]
- New-Star of Science and Technology
- Shaanxi Provience [2013KJXX-63]
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In this letter, we propose a new spatio-spectral Laplacian support vector machine (SS-LapSVM) for semi-supervised hyperspectral image classification. The clustering assumption on spectral vectors is used to formulate a manifold regularizer, and neighborhood spatial constraints of hyperspectral images are designed to construct a spatial regularizer. Moreover, a non-iterative optimization procedure is presented to solve this dual-regularized SVM, which makes rapid classification possible. By combining spatial and spectral information together, SS-LapSVM can avoid the speckle-like misclassification of hyperspectral images in the original Lap-SVM. The performance of SS-LapSVM is evaluated on AVIRIS image data taken over Indiana's Indian Pine, and the results show that it can achieve accurate and rapid classification with a small number of labeled data, and outperform state-of-the-art semi-supervised approaches.
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