Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not. To achieve a better result in generating the change map, many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminative ability. However, these methods still get lower performance because they ignore spatial information and scaling changes between objects, giving rise to blurry or wrong boundaries. In addition to these, they also neglect the interactive information of two different images. To alleviate these problems, we propose our network, the Scale and Relation-Aware Siamese Network (SARAS-Net) to deal with this issue. In this paper, three modules are proposed that include relation-aware, scale-aware, and cross-transformer to tackle the problem of scene change detection more effectively. To verify our model, we tested three public datasets, including LEVIR-CD, WHU-CD, and DSFIN, and obtained SoTA accuracy. Our code is available at https://github.com/f64051041/SARAS-Net.
@article{arxiv.2212.01287,
title = {SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection},
author = {Chao-Peng Chen and Jun-Wei Hsieh and Ping-Yang Chen and Yi-Kuan Hsieh and Bor-Shiun Wang},
journal= {arXiv preprint arXiv:2212.01287},
year = {2022}
}