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Journal of Applied Remote Sensing

Data field modeling and data description for hyperspectral target detection
Author(s): Da Liu; Jianxun Li
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Paper 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.

Paper Details

Date Published: 6 July 2016
PDF: 20 pages
J. Appl. Rem. Sens. 10(3) 035001 doi: 10.1117/1.JRS.10.035001
Published in: Journal of Applied Remote Sensing Volume 10, Issue 3
Show Author Affiliations
Da Liu, Shanghai Jiao Tong Univ. (China)
Jianxun Li, Shanghai Jiao Tong Univ. (China)

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