Hyperspectral Image Classification via Multiscale Joint Collaborative Representation With Locally Adaptive Dictionary

• Published in: IEEE geoscience and remote sensing letters Vol. 15; no. 1; pp. 112 - 116
• Main Authors: Yang, Jinghui, Qian, Jinxi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Collaboration; Dictionaries; Educational institutions; Hyperspectral classification; Hyperspectral imaging; Image classification; joint collaborative representation (CR); locally adaptive dictionary; Methods; multiscale; Representations; Support vector machines; Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2017.2776113

In this letter, a multiscale joint collaborative representation with locally adaptive dictionary (MLJCRC) method is proposed for hyperspectral image classification. Based on the joint collaborative representation model, instead of selecting only a single region scale, MLJCRC incorporates complementary contextual information into classification by multiplying different scales with distinct spatial structures and characteristics. Also, MLJCRC uses a locally adaptive dictionary to reduce the influence of irrelevant pixels on representation, which improves the classification accuracy. The results of experiments on Indian Pines data and Pavia University data demonstrate that the proposed method performs better than support vector machine, sparse representation classification, and other collaborative representation-based classifications.