RSAAE: Residual Self-Attention-Based Autoencoder for Hyperspectral Anomaly Detection

Abstract

Autoencoder (AE) has been widely used in the field of hyperspectral anomaly detection. It is assumed that the background can be reconstructed well, but the anomalies cannot. Hence, the pixels with larger reconstruction error are considered as anomalies. However, owing to the strong nonlinear representation ability of AE, it is difficult to distinguish between background and anomalies. To address this problem, we propose a Residual Self-Attention-based AutoEncoder (RSAAE) for hyperspectral anomaly detection. RSAAE consists of dense residual self-attention modules, an encoder, and a decoder. First, a novel residual self-attention module is designed, which can effectively extract the main features and weaken the ability of subsequent network to reconstruct anomalies, as well as preserve the original features to avoid the deterioration of network performance after the use of dense self-attention modules. Furthermore, inspired by manifold learning, we assume that the background is low-rank in the original space, and has the same property in the latent space after dimensionality reduction. We proposed a low-rank loss function to constrain the latent space, thereby suppressing anomaly reconstruction. Experiments on four real hyperspectral image (HSI) datasets showed that the proposed RSAAE method can produce more accurate detection results than eight popular methods.