Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 12, Issue 5, Pages 1028-1032Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2014.2375188
Keywords
Discriminative tensor representation; hyperspectral classification; spectral-spatial feature extraction
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Funding
- Natural Science Foundation of China [91338202, 61331018, 61305049, 61375024]
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We propose to integrate spectral-spatial feature extraction and tensor discriminant analysis for hyperspectral image classification. First, we apply remarkable spectral-spatial feature extraction approaches in the hyperspectral cube to extract a feature tensor for each pixel. Then, based on class label information, local tensor discriminant analysis is used to remove redundant information for subsequent classification procedure. The approach not only extracts sufficient spectral-spatial features from original hyperspectral images but also gets better feature representation owing to tensor framework. Comparative results on two benchmarks demonstrate the effectiveness of our method.
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