LAUSR

Due to variations in imaging conditions, spectra of the same type of ground objects usually exhibit certain discrepancies, leading to intraclass spectral distance increase and interclass distance decrease. As a result, classification accuracy is greatly affected, especially in cases with few labeled samples. For representation-based classifiers, the spectral variability within limited training samples is far from sufficient to represent diverse variations within testing ones. To handle this problem, a spectral variation augmented representation for hyperspectral imagery classification (SVARC) with few labeled samples is proposed in this article. First, a novel class-independent and -dependent components-based linear representation model (CICD-LRM) is proposed to emphasize the representation of spectral variation. Second, depending on spatial and spectral correlation, the CICD-LRM-guided global and local spectral variation extraction schemes are designed, and a fused spectral variation dictionary is constructed by concatenation. Finally, a classifier for hyperspectral images based on the CICD-LRM and spectral variation dictionary is proposed, and specifically, three different spectral variation reconstruction strategies are designed. Similar to most representation-based classifiers, a residual-driven decision is also employed in the proposed classifier. Comparative experiments are conducted with eight classical and state-of-the-art methods using two benchmark datasets. The experimental results demonstrate that the proposed SVARC method significantly outperforms the compared ones in cases with few labeled samples.