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Target detection for hyperspectral image based on multi-scale analysis

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Abstract

To further improve target detection performance of hyperspectral image, this paper presents a novel method named multi-scale analysis-based target detection (MATD). The proposed method first applies multi-scale wavelet analysis technology to extract multi-scale features of hyperspectral data. Then, these features are converted into a tensor form, and is processed and analyzed by using tensor analysis method. Through solving the tensor subspace, the reduced-dimension feature coefficients can be extracted. Finally, based on these feature coefficients, a better target detection result can be obtained by using the detection algorithm. Experimental results of real world hyperspectral data show that the proposed MATD method can effectively improve detection performance.

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Acknowledgements

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, Xi’an, Shanxi, 710025, China

    Yiting Wang, Zhigang Liu, Hongxia Wang & Daizhi Liu

  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. Zhigang Liu
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  4. Hongxia Wang
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  5. Daizhi Liu
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Correspondence to Yiting Wang.

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Wang, Y., Huang, S., Liu, Z. et al. Target detection for hyperspectral image based on multi-scale analysis. J Opt 46, 75–82 (2017). https://doi.org/10.1007/s12596-016-0334-5

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  • DOI: https://doi.org/10.1007/s12596-016-0334-5

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