Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection

TL;DR

This paper introduces a novel sketched multi-view subspace learning model for hyperspectral anomalous change detection, enhancing detection accuracy and computational efficiency by leveraging sketched representations and self-representation regularizers.

Contribution

The paper proposes the SMSL model that preserves key information and reduces complexity in hyperspectral change detection, addressing limitations of existing methods on large datasets.

Findings

Improved detection accuracy on benchmark datasets

Reduced computational complexity compared to traditional methods

Effective identification of small changes in hyperspectral images

Abstract

In recent years, multi-view 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 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, existing methods may fail to find those changes between the scenes, and end up with terrible detection results. In this paper, inspired by the sketched representation and multi-view subspace learning, a sketched multi-view subspace learning (SMSL) model is proposed for HSI anomalous change detection. The proposed model preserves major information from the image pairs and improves computational complexity by using a sketched representation matrix. Furthermore, the differences between scenes are extracted by utilizing 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.