气候变化领域集成服务门户(CCPortal): A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images

CCPortal

DOI	10.3390/rs16071169
	A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images
	Yao, Shunyu; Wang, Han; Su, Yalu; Li, Qing; Sun, Tao; Liu, Changjun; Li, Yao; Cheng, Deqiang
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	7
卷号	16 期号:7
英文摘要	Natural hazards are considered to have a strong link with climate change and human activities. With the rapid advancements in remote sensing technology, real-time monitoring and high-resolution remote-sensing images have become increasingly available, which provide precise details about the Earth's surface and enable prompt updates to support risk identification and management. This paper proposes a new network framework with Transformer architecture and a Residual network for detecting the changes in high-resolution remote-sensing images. The proposed model is trained using remote-sensing images from Shandong and Anhui Provinces of China in 2021 and 2022 while one district in 2023 is used to test the prediction accuracy. The performance of the proposed model is evaluated by using five matrices and further compared to both convention-based and attention-based models. The results demonstrated that the proposed structure integrates the great capability of conventional neural networks for image feature extraction with the ability to obtain global context from the attention mechanism, resulting in significant improvements in balancing positive sample identification while avoiding false positives in complex image change detection. Additionally, a toolkit supporting image preprocessing is developed for practical applications.
英文关键词	change detection; Vision Transformer (ViT); remote-sensing image with high resolution; convolutional neural networks (CNN); self-attention mechanism
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001200803200001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288037
作者单位	China Institute of Water Resources & Hydropower Research; Tsinghua University; Henan University
推荐引用方式 GB/T 7714	Yao, Shunyu,Wang, Han,Su, Yalu,et al. A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images[J],2024,16(7).
APA	Yao, Shunyu.,Wang, Han.,Su, Yalu.,Li, Qing.,Sun, Tao.,...&Cheng, Deqiang.(2024).A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images.REMOTE SENSING,16(7).
MLA	Yao, Shunyu,et al."A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images".REMOTE SENSING 16.7(2024).