UADNet: A Joint Unmixing and Anomaly Detection Network Based on Deep Clustering for Hyperspectral Image

With the lack of sufficient prior information, unsupervised hyperspectral unmixing (HU) has been a preprocessing step in the hyperspectral image (HSI) processing pipeline, which can provide the types of material and corresponding abundance information of HSI, to further provide assistance for downstream higher level semantic tasks to overcome the limitation caused by mixed pixels. However, the unmixing results obtained by current unsupervised HU methods are unstable and unprecise under the guidance of the least reconstruction error (RE), which have no consistency with the performance of high-level tasks. To solve this problem, this article takes the hyperspectral anomaly detection (HAD) as an entry point and proposes a novel algorithm based on deep clustering which can jointly perform HU and HAD in an end-to-end manner. A mutual feedback mechanism is formed between the upstream HU process and the downstream HAD process, and through joint optimization, both two tasks can achieve relatively good performances. However, the low dimensional abundance has a limited representation, which may lead to the increase of false alarm rate. To overcome this limitation, the principal components (PCs) of HSI are fused with the abundance to enhance the representation ability. Moreover, we use the reweighted reconstruction loss strategy to enhance the role of anomalies in the HU process. Experiments performed on several real datasets verify the rationality and superiority of the proposed UADNet algorithm.