Hyperspectral Anomaly Detection via Image Super-Resolution Processing and Spatial Correlation

Abstract

Anomaly detection is a key problem in hyperspectral image (HSI) analysis with important remote sensing applications. Traditional methods for hyperspectral anomaly detection are mostly based on the distinctive statistical features of the HSIs. However, the anomaly-detection performance of these methods has been negatively impacted by two major limitations: 1) failure to consider the spatial pixel correlation and the ground-object correlation and 2) the existence of the mixing pixels caused by both lower spatial resolution and higher spectral resolution, which leads to higher false-alarm rates. In this article, these two problems are largely solved through a novel hyperspectral anomaly-detection method based on image super-resolution (SR) and spatial correlation. The proposed method encompasses two innovative ideas. First, based on the spectral variability in the anomaly targets, an extended linear mixing model can be obtained with more accurate ground-object information. Then, image SR is used to improve the spatial resolution of the HSIs by injecting the ground-object information from the mixing model. This alleviates the effect of mixed pixels on anomaly detection. Second, spatial correlation is exploited jointly with the global Reed-Xiaoli (GRX) method and the ground-object correlation detection for anomaly detection. Experimental results show that the proposed method not only effectively improves the hyperspectral spatial resolution and reduces the false-alarm rate but also increases the detectability with the spatial correlation information. Furthermore, the results for the real HSIs demonstrate that the proposed method achieves higher rates of anomaly detection with lower false-alarm rates.