Spatial–Spectral Joint Reconstruction With Interband Correlation for Hyperspectral Anomaly Detection

Hyperspectral image (HSI) anomaly detection is an important task in remote sensing domain. In recent years, many scholars have been addicted to constructing deep network-based methods for hyperspectral anomaly detection and have developed numerous related methods. Many of them are designed based on autoencoder, which aims to reconstruct a stable background to identify anomalies. However, these autoencoder-based methods suffer from some problems, such as ignoring the inter-band correlation in HSI. That is, the hyperspectral image presents spectral similarity as well as redundancy between the contiguous bands, which would affect the reconstruction of the HSI. Moreover, the current anomaly detectors lack the use of spatial contextual information that exists in the pixel neighbor region when constructing the detector. To tackle these problems, this study presents a spatial-spectral joint reconstruction with the inter-band correlation based anomaly detector (denoted as SSRICAD) for hyperspectral images. We first divide the original HSI into several sub-HSIs by a band cross-grouping strategy to reduce the redundancy and impose the inter-band correlation constraint into the reconstruction process. Then, an outlier removal constraint is added to alleviate anomaly contamination, which could help rebuild a more stable and pure background component. Finally, spatial information is extracted from the pixel neighbor region to contribute to the spatial-spectral joint reconstruction and further enhance detection performance. Extensive experiments on three benchmark hyperspectral datasets indicate that the proposed SSRICAD can achieve superior performance in anomaly detection.