TRSD: tensor spatial reconstruction and spectral metric decision fusion for hyperspectral anomaly detection with noise

Abstract

The unique and detailed spectral information in hyperspectral images (HSI) provides an advantage for distinguishing different targets in anomaly detection (AD). However, most traditional HSI-AD methods primarily focus on the inherent spectral structure information, often overlooking the strong spatial-spectral synergy present in HSI. An increase in spectral resolution typically leads to a decrease in the number of photons received per channel, which increases the likelihood of correlated noise during image formation. To address these issues and significantly improve detection performance, a method called Tensor Space Reconstruction and Spectral Local Correlation Metric Decision Fusion (TRSD) is proposed for HSI-AD in the presence of noise. First, three-dimensional principal component (PC) extraction, based on information entropy, is performed to obtain a denoised purified image for reconstruction. The initial feature detection image is generated by calculating the purified image using the local Mahalanobis distance. To compensate for the loss of spectral information caused by PC analysis in the spectral dimension during Tucker reconstruction, the feature map is extracted using the local spectral correlation metric. Finally, the two detection feature images are adaptively fused to generate the final AD image, which highlights anomaly targets and improves detection accuracy.The proposed algorithm is experimentally validated through comparisons with current typical AD algorithms, using real HSIs captured in four different complex noise-added scenarios. The effectiveness of the algorithm is demonstrated through experiments. The source code for TRSD will be made publicly available at https://github.com/muzhenhuam/TRSD.