Spectral–Spatial Feature Extraction for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection faces various levels of difficulty due to the high dimensionality of hyperspectral images (HSIs), redundant information, noisy bands, and the limited capability of utilizing spectral-spatial information. In this paper, we address these problems and propose a novel approach, called spectral-spatial feature extraction (SSFE), which is based on two main aspects. In the spectral domain, we assume that the anomalous pixels are rarely present and all (or most) of the samples around the anomalies belong to background (BKG). Using this fact, we introduce a suppression function to construct a discriminative feature space and utilize a deep brief network to learn spectral representation and abstraction automatically that are used as inputs to the Mahalanobis distance (MD)-based detector. In the spatial domain, the anomalies appear as a small area grouped by pixels with high correlation among them compared to BKG. Therefore, the objects appearing as a small area are extracted based on attribute filtering, and a guided filter is further employed for local smoothness. More specifically, we extract spatial features of anomalies only from one single band obtained by fusing all bands in the visible wavelength range. Finally, we detect anomalies by jointly considering the spectral and spatial detection results. Several experiments are performed, which show that our proposed method outperforms the state-of-the-art methods.