Abstract:
In recent years, deep learning technology has emerged as the primary research focus in the field of hyperspectral anomaly detection (HAD) and has demonstrated satisfactor...Show MoreMetadata
Abstract:
In recent years, deep learning technology has emerged as the primary research focus in the field of hyperspectral anomaly detection (HAD) and has demonstrated satisfactory detection performance. Existing deep learning-based methods mainly utilize reconstruction errors as criteria for anomaly detection. However, they lack effective suppression of anomaly information in the background reconstruction process, and encounter challenges in addressing scenarios involving the coexistence of multiscale anomalies, which limits the performance of HAD. In order to reconstruct clean and reliable background images, this article proposes a Triple-UNet with dual-window convolution called TDWCNet for HAD. Specifically, we introduce a dual-window convolution that shields pixels within the inner window and only utilizes pixels between the inner and outer windows to reconstruct the central pixel. Based on the dual-window convolution, we construct the DWCBlock module, which serves as the core component for background reconstruction. To address the coexistence of multiscale anomalies, the Triple-UNet structure is designed, which organically combines three DWCBlock modules to gradually eliminate abnormal pixels that are inadvertently reconstructed due to inappropriate convolution kernels. Furthermore, adaptive mean-squared error (mse) and structural similarity index (SSIM) losses are employed to suppress anomaly reconstruction. Extensive experiments conducted on four publicly available datasets demonstrate that TDWCNet achieves satisfactory detection performance. The codes of this work will be available for the sake of reproducibility at: https://github.com/szc2277/TDWCNet-HAD.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 62)