# Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection

arXiv: https://arxiv.org/abs/2205.11395 · HTML: https://mlsanity.com/paper/2205.11395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 3 of 3)

- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Hyperspectral Anomaly Change Detection Based on Auto-encoder](https://mlsanity.com/paper/2010.14119/) (2020)

## Built on by (most cited first, 1 of 1)

- [HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection](https://mlsanity.com/paper/2207.09634/) (2022)

## Abstract (arXiv)

Hyperspectral anomalous change detection has been a challenging task for its emphasis on the dynamics of small and rare objects against the prevalent changes. In this paper, we have proposed a Multi-Temporal spatial-spectral Comparison Network for hyperspectral anomalous change detection (MTC-NET). The whole model is a deep siamese network, aiming at learning the prevalent spectral difference resulting from the complex imaging conditions from the hyperspectral images by contrastive learning. A three-dimensional spatial spectral attention module is designed to effectively extract the spatial semantic information and the key spectral differences. Then the gaps between the multi-temporal features are minimized, boosting the alignment of the semantic and spectral features and the suppression of the multi-temporal background spectral difference. The experiments on the "Viareggio 2013" datasets demonstrate the effectiveness of proposed MTC-NET.