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A real-time unsupervised background extraction-based target detection method for hyperspectral imagery

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Abstract

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-time processing capability of the proposed system.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 91638201, 41722108, 41325004 and 41571349.

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Authors and Affiliations

  1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China

    Cong Li, Lianru Gao, Yuanfeng Wu & Bing Zhang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Cong Li & Bing Zhang

  3. The College of Computer Science and Software Engineering, Computer Vision Research Institute, Shenzhen University, Shenzhen, 518060, China

    Lianru Gao

  4. Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politecnica de Cáceres, University of Extremadura, Cáceres, Spain

    Javier Plaza & Antonio Plaza

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  1. Cong Li
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Correspondence to Lianru Gao.

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Li, C., Gao, L., Wu, Y. et al. A real-time unsupervised background extraction-based target detection method for hyperspectral imagery. J Real-Time Image Proc 15, 597–615 (2018). https://doi.org/10.1007/s11554-017-0742-z

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