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A Target Detection Method for Hyperspectral Imagery Based on Two-Time Detection

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Abstract

To solve the low detection efficiency problem of Constrained Energy Minimization (CEM) method used for hyperspectral remote sensing imagery, this paper firstly presents two improved detection methods: principal component CEM (PCCEM) and matrix taper CEM (MTCEM). Then, based on these two methods, a more optimized Two-Time detection (TTD) method is proposed. Primarily, the targets of interest in the hyperspectral image are detected by using the PCCEM and MTCEM method. Then the autocorrelation matrix of non-target pixels is estimated according to the target detection results. Finally, based on this autocorrelation matrix, a new weight vector is constructed for the second detection. Under the effect of this new weight vector, the output energy of the target can be kept at unity and the output energy of the background is suppressed at the same time. Then, the improvement of target detection result can be realized. Experimental results on a real world hyperspectral data show the efficiency of the proposed TTD method to improve the detection performance.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under project No. 41174093 and No. 41574008.

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Authors and Affiliations

  1. Xi’an Research Institute of Hi-Tech, Hongqing Town, Xi’an, 710025, People’s Republic of China

    Yiting Wang, Daizhi Liu & Hongxia Wang

  2. Xijing University, Xi’an, 710123, China

    Shiqi Huang

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  1. Yiting Wang
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  2. Shiqi Huang
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  3. Daizhi Liu
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  4. Hongxia Wang
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Correspondence to Yiting Wang.

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Wang, Y., Huang, S., Liu, D. et al. A Target Detection Method for Hyperspectral Imagery Based on Two-Time Detection. J Indian Soc Remote Sens 45, 239–246 (2017). https://doi.org/10.1007/s12524-016-0593-2

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  • DOI: https://doi.org/10.1007/s12524-016-0593-2

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