Nonlinear classification of multispectral imagery using representation-based classifiers
The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers, sparse representation-based classification (SRC) and Nearest Regularized Subspace (NRS) are evaluated on the generated datasets. The results show our approach can outperform other nonlinear method such as the traditional kernel method in terms of classification accuracy and computational cost.