Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 14, Issue -, Pages 2270-2283Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2021.3052968
Keywords
Hyperspectral imaging; Feature extraction; Visualization; Anomaly detection; Object detection; Information filters; Image color analysis; Anomaly detection (AD); background subtraction; curvature filter; hyperspectral image (HSI); hyperspectral visual attention model (HVAM)
Categories
Funding
- 111 Project [B17035]
- National Natural Science Foundation of China [51801142]
- Fundamental Research Funds for the CentralUniversities [JB180502]
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The paper introduces a strategy for hyperspectral anomaly detection using visual attention model and adaptive weight for background subtraction, which shows promising results in improving detection performance.
Anomaly detection (AD) in hyperspectral target detection is of particular interest because no prior knowledge of ground object spectra is required. However, it is difficult to utilize the salient features of hyperspectral image (HSI) and mitigate the effects of noise in hyperspectral AD, which greatly limits the detection performance. Here, we report a strategy to implement hyperspectral AD by visual attention model and background subtraction with adaptive weight. Through band selection method, the most discriminating bands are selected as the input images for subsequent processing. Then, the hyperspectral visual attention model is introduced, for the first time, into hyperspectral AD for extracting the salient feature map of the input images. Furthermore, the background subtraction process that can reduce the background and noise in the salient feature map is developed via curvature filter. Using this operation, the initial anomaly area map is obtained. Finally, incorporating with the spectral information, an adaptive weight map is applied to the initial anomaly area map to further suppress the background. In the experiment, the proposed method is compared with seven other state-of-the-art methods on synthetic and real-world HSI. Most importantly, the results demonstrate that the proposed method is effective and performs better than alternative methods. We believe that this method can open a new avenue of visual processing methods for hyperspectral AD.
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