Sketched Multiview Subspace Learning for Hyperspectral Anomalous Change Detection

In recent years, multiview subspace learning has been garnering increasing attention. It aims to capture the inner relationships of the data that are collected from multiple sources by learning a unified representation. In this way, comprehensive information from multiple views is shared and preserved for the generalization processes. As a special branch of temporal series hyperspectral image (HSI) processing, the anomalous change detection (ACD) task focuses on detecting very small changes among different temporal images. However, when the volume of datasets is very large or the classes are relatively comprehensive, the existing methods may fail to find those changes between the scenes, and end up with terrible detection results. In this article, inspired by the sketched representation and multiview subspace learning, a sketched multiview subspace learning (SMSL) model is proposed for HSI ACD. The proposed model preserves major information from the image pairs and improves the computational complexity using a sketched representation matrix. Furthermore, the differences between scenes are extracted using the specific regularizer of the self-representation matrices. To evaluate the detection effectiveness of the proposed SMSL model, experiments are conducted on a benchmark hyperspectral remote sensing dataset and a natural hyperspectral dataset and compared with other state-of-the-art approaches. The code of the proposed method will be available at https://github.com/ShizhenChang/SMSL .