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Proceedings Paper

Superpixel sparse representation for target detection in hyperspectral imagery
Author(s): Chunhua Dong; Masoud Naghedolfeizi; Dawit Aberra; Hao Qiu; Xiangyan Zeng
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Paper Abstract

Sparse Representation (SR) is an effective classification method. Given a set of data vectors, SR aims at finding the sparsest representation of each data vector among the linear combinations of the bases in a given dictionary. In order to further improve the classification performance, the joint SR that incorporates interpixel correlation information of neighborhoods has been proposed for image pixel classification. However, SR and joint SR demand significant amount of computational time and memory, especially when classifying a large number of pixels. To address this issue, we propose a superpixel sparse representation (SSR) algorithm for target detection in hyperspectral imagery. We firstly cluster hyperspectral pixels into nearly uniform hyperspectral superpixels using our proposed patch-based SLIC approach based on their spectral and spatial information. The sparse representations of these superpixels are then obtained by simultaneously decomposing superpixels over a given dictionary consisting of both target and background pixels. The class of a hyperspectral pixel is determined by a competition between its projections on target and background subdictionaries. One key advantage of the proposed superpixel representation algorithm with respect to pixelwise and joint sparse representation algorithms is that it reduces computational cost while still maintaining competitive classification performance. We demonstrate the effectiveness of the proposed SSR algorithm through experiments on target detection in the in-door and out-door scene data under daylight illumination as well as the remote sensing data. Experimental results show that SSR generally outperforms state of the art algorithms both quantitatively and qualitatively.

Paper Details

Date Published: 5 May 2017
PDF: 8 pages
Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 102110E (5 May 2017); doi: 10.1117/12.2262147
Show Author Affiliations
Chunhua Dong, Fort Valley State Univ. (United States)
Masoud Naghedolfeizi, Fort Valley State Univ. (United States)
Dawit Aberra, Fort Valley State Univ. (United States)
Hao Qiu, Fort Valley State Univ. (United States)
Xiangyan Zeng, Fort Valley State Univ. (United States)


Published in SPIE Proceedings Vol. 10211:
Compressive Sensing VI: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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