Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

• Published in: IEEE geoscience and remote sensing letters Vol. 11; no. 7; pp. 1235 - 1239
• Main Authors: Xiaoxia Sun, Qing Qu, Nasrabadi, Nasser M., Tran, Trac D.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Collaboration; Dictionaries; hyperspectral image (HSI); Hyperspectral imaging; Laplace equations; Sparse matrices; sparse representation; structured priors; Support vector machines; hyperspectral image (HSI); structured priors; Classification; sparse representation
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2013.2290531

Pixelwise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results compared with that of traditional classifiers such as the support vector machine. Recently, by incorporating additional structured sparsity priors, the second-generation SRCs have appeared in the literature and are reported to further improve the performance of HSI. These priors are based on exploiting the spatial dependences between the neighboring pixels, the inherent structure of the dictionary, or both. In this letter, we review and compare several structured priors for sparse-representation-based HSI classification. We also propose a new structured prior called the low-rank (LR) group prior, which can be considered as a modification of the LR prior. Furthermore, we will investigate how different structured priors improve the result for the HSI classification.