Dual Feature Extraction Network for Hyperspectral Image Analysis

Abstract Hyperspectral anomaly detection (HAD) is a research endeavor of high practical relevance within remote sensing scene interpretation. In this work, we propose an unsupervised approach, dual feature extraction network (DFEN) for HAD, to gradually build up ever-greater discrimination between the original data and background. In particular, we impose an end-to-end discriminative learning loss on two networks. Among them, adversarial learning aims to keep the original spectrum while Gaussian constrained learning intends to learn the background distribution in the potential space. To extract the anomaly, we calculate spatial and spectral anomaly scores based on mean squared error (MSE) spatial distance and orthogonal projection divergence (OPD) spectral distance between two latent feature matrices. Finally, the comprehensive detection result is obtained by a simple dot product between two domains to further reduce the false alarm rate. Experiments have been conducted on eight real hyperspectral data sets captured by different sensors over different scenes, which show that the proposed DFEN method is superior to other compared methods in detection accuracy or false alarm rate.