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Dictionary learning based target detection for hyperspectral image
Author(s): Xiaorong Zhang; Bingliang Hu; Zhibin Pan; Xi Zheng
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Paper Abstract

Target detection of hyperspectral image has always been a hot research topic, especially due to its important applications in military and civilian remote sensing. This paper employs the idea of classification and proposes a novel detection framework which incorporates dictionary learning and discriminative information. Due to the fact that target pixels lie in different subspace with background pixels, a novel detection model is proposed. In addition, a linear kernel is applied to project the image data into high-dimensional space, separating the target pixels and background pixels. Synthetic image and popular real hyperspectral image are used to evaluate our algorithm. Experimental results indicate that our proposed detector outperforms the traditional detection methods.

Paper Details

Date Published: 12 March 2019
PDF: 14 pages
Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110232D (12 March 2019); doi: 10.1117/12.2519943
Show Author Affiliations
Xiaorong Zhang, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Sciences (China)
Bingliang Hu, Xi'an Institute of Optics and Precision Mechanics (China)
Zhibin Pan, Xi'an Jiaotong Univ. (China)
Xi Zheng, Institute of Earth Environment (China)


Published in SPIE Proceedings Vol. 11023:
Fifth Symposium on Novel Optoelectronic Detection Technology and Application
Qifeng Yu; Wei Huang; You He, Editor(s)

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