# Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection

arXiv: https://arxiv.org/abs/2210.04271 · HTML: https://mlsanity.com/paper/2210.04271/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)
- [GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection](https://mlsanity.com/paper/1905.01662/) (2019)
- [Feature Concatenation Multi-view Subspace Clustering](https://mlsanity.com/paper/1901.10657/) (2019)
- [Sketched Subspace Clustering](https://mlsanity.com/paper/1707.07196/) (2017)
- [Hyperspectral Anomaly Change Detection Based on Auto-encoder](https://mlsanity.com/paper/2010.14119/) (2020)
- [HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection](https://mlsanity.com/paper/2207.09634/) (2022)
- [Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds](https://mlsanity.com/paper/1805.06299/) (2018)
- [Kernel Anomalous Change Detection for Remote Sensing Imagery](https://mlsanity.com/paper/2012.04920/) (2020)
- [Multi-view Common Component Discriminant Analysis for Cross-view Classification](https://mlsanity.com/paper/1805.05029/) (2018)
- [Exclusivity Regularized Machine](https://mlsanity.com/paper/1603.08318/) (2016)

## Built on by (most cited first, 3 of 3)

- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection](https://mlsanity.com/paper/2304.08687/) (2023)
- [EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection](https://mlsanity.com/paper/2303.13753/) (2023)

## Abstract (arXiv)

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.