Data field modeling and data description for hyperspectral target detection

Da Liu; Jianxun Li

doi:10.1117/1.JRS.10.035001

6 July 2016 Data field modeling and data description for hyperspectral target detection

Da Liu, Jianxun Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 035001 (July 2016). https://doi.org/10.1117/1.JRS.10.035001

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.

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Da Liu and Jianxun Li "Data field modeling and data description for hyperspectral target detection," Journal of Applied Remote Sensing 10(3), 035001 (6 July 2016). https://doi.org/10.1117/1.JRS.10.035001

Published: 6 July 2016

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