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17 August 2023 Noise-robust hyperspectral anomaly detection via relative total variation collaborative representation
Chensong Yin, Anni Liu, Weitao Sun, Mingjie Wang, Leitao Gao
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Proceedings Volume 12757, 3rd International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2023); 127573F (2023) https://doi.org/10.1117/12.2690269
Event: 3rd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2023), 2023, Kunming, China
Abstract
A novel noise-robust hyperspectral anomaly detector based on relative total variation collaborative representation is proposed to settle the problem of low detection probability of collaborative representation detector under the condition of noisy hyperspectral image or large and irregular anomaly. The relative total variation method is employed to preprocess hyperspectral image and to obtain the pure structure information of hyperspectral image, which features lower intra-class difference and higher inter-class difference. Subsequently, the collaborative representation detector can be carried out, effectively alleviating the abnormal contamination of local background. Superior anomaly detection performance is obtained by the proposed algorithm, and the dependent of anomaly detection accuracy on the size of double-windows is greatly reduced.
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Chensong Yin, Anni Liu, Weitao Sun, Mingjie Wang, and Leitao Gao "Noise-robust hyperspectral anomaly detection via relative total variation collaborative representation", Proc. SPIE 12757, 3rd International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2023), 127573F (17 August 2023); https://doi.org/10.1117/12.2690269
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KEYWORDS
Hyperspectral imaging
Detection and tracking algorithms
Image processing
Object detection
Image visualization
Target detection
Visualization
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