@inproceedings{d72984ba00894b7dadf0f9aa887d11d5,
title = "Hybrid Transformer Network for Change Detection Under Self-Supervised Pretraining",
abstract = "This paper presents a Siamese network architecture based on a multi-scale hybrid convolution-Transformer (CTUNet) for Change Detection (CD) in a pair of co-registered optical remote sensing images. Different form CD frameworks based on convolution neural networks (CNNs) and pure Transformer networks, this method combines a convolution-Transformer hybrid encoder with a multi-scale change information extraction decoder in a Siamese network architecture. It overcomes the inherent limitations of CNN and Transformer and effectively integrates the multi-scale information required for accurate CD. To learn better discriminative representations from various scales, we propose a masked auto-encoder scheme (CTMAE) to adapt to building targets with varying morphological scales, further unleashing the potential of CTUNet. Experiments on two CD datasets show that the proposed self-supervised pre-trained hybrid convolution-Transformer CTUNet architecture achieves better CD performance than previous methods.",
keywords = "Change detection, hybrid convolution-Transformer, masked auto-encoder, multi-scale fusion, remote sensing",
author = "Yongjing Cui and Yin Zhuang and Shan Dong and Xinyi Zhang and Peng Gao and He Chen and Liang Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",
year = "2023",
doi = "10.1109/IGARSS52108.2023.10281977",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6652--6655",
booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}