Image and Signal Processing Methods

Data field modeling and data description for hyperspectral target detection

[+] Author Affiliations
Da Liu, Jianxun Li

Shanghai Jiao Tong University, School of Electronic, Information and Electrical Engineering, No. 800 Dongchuan Road, Minhang District, Shanghai 200240, China

J. Appl. Remote Sens. 10(3), 035001 (Jul 06, 2016). doi:10.1117/1.JRS.10.035001
History: Received November 23, 2015; Accepted June 14, 2016
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Abstract.  Target detection is an important issue in hyperspectral remote sensing image processing. This paper proposes a method for hyperspectral target detection using data field theory to simulate the data interaction in hyperspectral images (HSIs). We then build a data field model to unify spectral and spatial information. Furthermore, a support vector detector based on a data field model is proposed. Compared with traditional methods, our method achieves superior performance for hyperspectral target detection, and it describes a target class with a more accurate and flexible high potential region. Moreover, in contrast to traditional hyperspectral detectors, the proposed method achieves integrated spectral–spatial target detection and shows superior robustness to signal-noise-ratio decline and spectral resolution degradation. The experimental results show that our method is more accurate and efficient for target detection problems in HSIs.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Da Liu and Jianxun Li
"Data field modeling and data description for hyperspectral target detection", J. Appl. Remote Sens. 10(3), 035001 (Jul 06, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.035001


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