Discriminative Reconstruction for Hyperspectral Anomaly Detection With Spectral Learning

Recently, autoencoder (AE)-based anomaly detection has drawn considerable interest in hyperspectral image (HSI) analysis. In this article, we propose a novel discriminative reconstruction method for hyperspectral anomaly detection images with spectral learning (SLDR). The proposed algorithm has the following innovations. First, we use the spectral error map (SEM) to detect anomalies because the SEM can preferably reflect the spectral similarity of each pixel between the input and the reconstruction. Second, the loss function of the proposed SLDR model additionally introduces the spectral angle distance (SAD), which constrains the model to generate a reconstruction having greater spectral similarity to the input. Third, a constraint is imposed on the encoder, forcing it to generate latent variables that obey a unit Gaussian distribution, which helps the decoder to reconstruct a better background with respect to the input. Compared with the Reed-Xiaoli (RX), collaborative representation detection (CRD), attribute and edge-preserving filtering-based anomaly detection (AED) and adversarial autoencoder-based anomaly detection (AAE), through two real HSI data sets, the detection performance of the proposed SLDR method is found to be competitive.