Hyperspectral Anomaly Detection based on Autoencoder using Superpixel Manifold Constraint

Abstract

In the field of hyperspectral anomaly detection, autoencoder (AE) have become a hot research topic due to their unsupervised characteristics and powerful feature extraction capability. However, autoencoders do not keep the spatial structure information of the original data well during the training process, and is affected by anomalies, resulting in poor detection performance. To address these problems, a hyperspectral anomaly detection method based on autoencoders with superpixel manifold constraints is proposed. Firstly, superpixel segmentation technique is used to obtain the superpixels of the hyperspectral image, and then the manifold learning method is used to learn the embedded manifold that based on the superpixels. Secondly, the learned manifold constraints are embedded in the autoencoder to learn the potential representation, which can maintain the consistency of the local spatial and geometric structure of the hyperspectral images (HSI). Finally, anomalies are detected by computing reconstruction errors of the autoencoder. Extensive experiments are conducted on three datasets, and the experimental results show that the proposed method has better detection performance than other hyperspectral anomaly detectors.