Yang, Z.; Zhao, R.; Meng, X.; Yang, G.; Sun, W.; Zhang, S.; Li, J. A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection. Remote Sens.2024, 16, 3036.
Yang, Z.; Zhao, R.; Meng, X.; Yang, G.; Sun, W.; Zhang, S.; Li, J. A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection. Remote Sens. 2024, 16, 3036.
Yang, Z.; Zhao, R.; Meng, X.; Yang, G.; Sun, W.; Zhang, S.; Li, J. A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection. Remote Sens.2024, 16, 3036.
Yang, Z.; Zhao, R.; Meng, X.; Yang, G.; Sun, W.; Zhang, S.; Li, J. A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection. Remote Sens. 2024, 16, 3036.
Abstract
Existing methods of hyperspectral anomaly detection still face several challenges: 1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; 2) The ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; 3) Current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind spot network based on multi-scale blind spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial-spectral joint module and a background feature attention mechanism to enhance the interaction between spatial-spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that integrates pixel shuffle down-sampling (PD) operation with spatial spectral joint screening to address the issue of anomalous identity map-ping and facilitate finite-scale mask convolution to adapt to the detection of more scale targets. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the art methods.
Computer Science and Mathematics, Geometry and Topology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
