Abstract

Hyperspectral anomaly detection aims to classify the anomalous objects in the scene. However, the spatial resolution of the hyperspectral images is relatively low, leading to inaccurate detection of abnormal pixels. Existing methods either ignore the low-resolution problem or leverage super-resolution models to reconstruct the global image to detect abnormal pixels. We claim that reconstructing super-resolution of the global image is unnecessary, while the area where the abnormal target is located should be paid more attention to be reconstructed. In this paper, we propose a super-resolution reconstruction with an attention mechanism for hyperspectral anomaly detection. Our method can automatically extract additional high-frequency information from low-spatial-resolution images and detect abnormal pixels simultaneously. Furthermore, the spatial-channel attention mechanism is adopted to select significant features for reconstructing super-resolution images by assigning different weights to different channels and different spatial–spectral locations. Finally, a regularized join loss function is proposed that balances different tasks by adjusting the relative weight. The experimental results on the public hyperspectral real datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

© 2021 Optical Society of America

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References

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Data Availability

Data underlying some of the results presented in this paper are available in Refs. [42], [43]. Other generated data are not publicly available at this time but may be obtained from the authors upon reasonable request.

42. Jet Propulsion Laboratory, California Institute of Technology, “Hyperspectral images for the Mexico Deepwater Horizon oil spill,” AVIRIS: Airborne Visible/Infrared Imaging Spectrometer, accessed 2021, http://aviris.jpl.nasa.gov/html/gulfoilspill.html.

43. X. Kang, “AVIRIS sensor datasets for urban Gainesville areas,” xudongkang.weebly.com, accessed 2021, http://xudongkang.weebly.com/data-sets.html.

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