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Efficient Probabilistic Collaborative Representation-Based Classifier for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2019.2906839.png)
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Abstract
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This letter presents an efficient probabilistic collaborative representation-based classifier (PROCRC) for hyperspectral image classification. Its performance is evaluated on different types of spatial features of hyperspectral imagery (HSI) including shape feature (i.e., extended multiattribute feature), global feature (i.e., Gabor feature), and local feature [i.e., local binary pattern (LBP)]. Compared with the original collaborative representation classifier (CRC), the proposed PROCRC offers superior classification performance. The Tikhonov regularized versions of CRC have excellent classification performance but their computational cost is high. The experimental results show that the PROCRC can yield comparable classification accuracy but with much lower computational cost.
Keywords:
Cathode ray tubes
Training
Hyperspectral imaging
Testing
Feature extraction
Collaboration
Support vector machines
Classification
collaborative representation
hyperspectral imagery (HSI)
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