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Grouped Collaborative Representation for Hyperspectral Image Classification Using a Two-Phase Strategy
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DOI：10.1109/LGRS.2021.3070074.png)
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Abstract
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This letter proposes a two-phase strategy-based grouped collaborative representation classifier (CRC) for hyperspectral image classification. Specifically, a spectral correlation-based CRC (SCCR) is proposed in the first phase, which considers a regularization term that uses the spectral correlations between the test sample and training samples. The representation coefficient vector generated by SCCR is then transformed into class-specific group weights. In the second phase, we integrate group weights and spectral correlations into the CRC and propose a grouped CRC (GCRC). Experimental results obtained from three real hyperspectral data sets demonstrate that the proposed SCCR and GCRC can provide better classification performance over other state-of-the-art representation-based classifiers.
Keywords:
Training
Collaboration
Testing
Hyperspectral imaging
Correlation
Support vector machines
Dictionaries
Collaborative representation (CR)
grouped CR
hyperspectral classification
hyperspectral image (HSI)
two-phase strategy
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