LAUSR

Hyperspectral anomaly detection (AD) aims to detect objects significantly different from their surrounding background. Recently, many detectors based on autoencoder (AE) exhibited promising performances in hyperspectral AD tasks. However, the fundamental hypothesis of the AE-based detector that anomaly is more challenging to be reconstructed than background may not always be true in practice. We demonstrate that an AE could well reconstruct anomalies even without anomalies for training, because AE models mainly focus on the quality of sample reconstruction and do not care if the encoded features solely represent the background rather than anomalies. If more information is preserved than needed to reconstruct the background, the anomalies will be well reconstructed. This article proposes a cluster-memory augmented deep autoencoder via optimal transportation for hyperspectral anomaly detection (OTCMA) clustering for hyperspectral AD to solve this problem. The deep clustering method based on optimal transportation (OT) is proposed to enhance the features consistency of samples within the same categories and features discrimination of samples in different categories. The memory module stores the background’s consistent features, which are the cluster centers for each category background. We retrieve more consistent features from the memory module instead of reconstructing a sample utilizing its own encoded features. The network focuses more on consistent feature reconstruction by training AE with a memory module. This effectively restricts the reconstruction ability of AE and prevents reconstructing anomalies. Extensive experiments on the benchmark datasets demonstrate that our proposed OTCMA achieves state-of-the-art results. Besides, this article presents further discussions about the effectiveness of our proposed memory module and different criteria for better AD.