Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery
Zhao, Chunhui and Li, Xiaohui and Ren, Jinchang and Marshall, Stephen (2013) Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery. International Journal of Remote Sensing, 34 (24). pp. 8669-8684. ISSN 0143-1161 (https://doi.org/10.1080/01431161.2013.845924)
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Abstract
With increasing applications of hyperspectral imagery (HSI) in agriculture, mineralogy, military, and other fields, one of the fundamental tasks is accurate detection of the target of interest. In this article, improved sparse representation approaches using adaptive spatial support are proposed for effective target detection in HSI. For conventional sparse representation, an HSI pixel is represented as a sparse vector whose non-zero entries correspond to the weights of the selected training atoms from a structured dictionary. For improved sparse representation, spatial correlation and spectral similarity of adjacent neighbouring pixels are exploited as spatial support in this context. The size and shape of the spatial support is automatically determined using both adaptive window and adaptive neighbourhood strategies. Accordingly, a solution based on greedy pursuit algorithms is also given to solve the extended optimization problem in recovering the desired sparse representation. Comprehensive experiments on three different data sets using both visual inspection and quantitative evaluation are carried out. The results from these data sets have indicated that the proposed approaches help to generate improved results in terms of efficacy and efficiency.
ORCID iDs
Zhao, Chunhui, Li, Xiaohui, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194 and Marshall, Stephen ORCID: https://orcid.org/0000-0001-7079-5628;-
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Item type: Article ID code: 48405 Dates: DateEvent2013Published22 November 2013Published OnlineSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 03 Jun 2014 13:11 Last modified: 29 Aug 2024 00:41 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/48405