Hyperspectral Image Classification Using Weighted Joint Collaborative Representation

• Published in: IEEE geoscience and remote sensing letters Vol. 12; no. 6; pp. 1209 - 1213
• Main Authors: Xiong, Mingming, Ran, Qiong, Li, Wei, Zou, Jinyi, Du, Qian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Collaborative representation based classifier; Educational institutions; hyperspectral image (HSI) classification; Hyperspectral imaging; nearest regularized subspace (NRS) classifier; sparse representation based classifier; spectral-spatial information; Support vector machines; Training; spectral–spatial information; Collaborative representation based classifier; nearest regularized subspace (NRS) classifier; sparse representation based classifier; hyperspectral image (HSI) classification
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2015.2388703

Recently, representation-based classifiers have gained increasing interest in hyperspectral image (HSI) classification. In this letter, based on our previously developed joint collaborative representation (JCR) classifier, an improved version, which is called weighted JCR (WJCR) classifier, is proposed. JCR adopts the same weights when extracting spatial and spectral features from surrounding pixels. Differing from JCR, WJCR attempts to utilize more appropriate weights by considering the similarity between the center pixel and its surroundings. Experimental results using two real HSIs demon strate that the proposed WJCR outperforms the original JCR and some other traditional classifiers, such as the support vector machine (SVM), the SVM with a composite kernel, and simultaneous orthogonal matching pursuit.