Low-complexity multiple collaborative representations for hyperspectral image classification

Collaborative representation has been a popular classifier for hyperspectral image classification because it can offer excellent classification accuracy with a closed-form solution. Collaborative representation can be implemented using a dictionary with training samples of all-classes, or using class-specific sub-dictionaries. In either case, a testing pixel is assigned to the class whose training samples offer the minimum representation residual. The Collaborative Representation Optimized Classifier with Tikhonov regularization (CROCT) was developed to combine these two types of collaborative representations to achieve the balance for optimized performance. The class-specific collaborative representation involves inverse operation of matrices constructed from class-specific samples, and the all-class version requires inversion operation of the matrix constructed from all samples. In this paper, we propose a low-complexity CROCT to avoid redundant operations in all-class and class-specific collaborative representations. It can further reduce the computational cost of CROCT while maintaining its excellent classification performance.