Superpixel-Based Relaxed Collaborative Representation With Band Weighting for Hyperspectral Image Classification

Abstract

Representation learning methods, such as sparse representation (SR) and collaborative representation (CR), have been widely used in hyperspectral image classification. However, they merely considered the similarities between features. Due to the plentiful spatial and spectral information in hyperspectral images, the differences between features also need to be considered. Relaxed CR (RCR) is used in face recognition to accommodate the difference and similarity of features simultaneously. In this article, a novel method of RCR with band weighting based on superpixel segmentation is proposed for hyperspectral image classification. The <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {l}_{ \boldsymbol {2}}$ </tex-math></inline-formula> norm on band coefficients and global average coefficients is exploited to ensure the similarity, and the variance determines the specific coefficient-related weight of each band. The training set is selected from each superpixel, which is considered as a subgraph rather than independent pixels. It is favorable for concentrating on the difference between similar bands since the samples in each superpixel are of high similarity. Furthermore, extended multiattribute profile (EMAP) features, Gabor features, and local binary pattern (LBP) features are employed to increase the diversity of features; thus, a method of multifeatures&#x2019; RCR based on superpixels is proposed. Three typical data are used to validate the related algorithms. The experiments demonstrate that the proposed algorithms can effectively improve classification accuracy compared to state-of-the-art classifiers.