CA-BIT: A Change Detection Method of Land Use in Natural Reserves

Natural reserves play a leading role in safeguarding national ecological security. Remote sensing change detection (CD) technology can identify the dynamic changes of land use and warn of ecological risks in natural reserves in a timely manner, which can provide technical support for the management...

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Bibliographic Details
Published in:Agronomy (Basel) 2023-03, Vol.13 (3), p.635
Main Authors: Jia, Bin, Cheng, Zhiyou, Wang, Chuanjian, Zhao, Jinling, An, Ning
Format: Article
Language:English
Subjects:
Agricultural land
Analysis
bitemporal image transformer
Change detection
China
Computational linguistics
Deep learning
Environmental security
Land use
Language processing
Methods
Natural language interfaces
natural reserves
Nature reserves
Protected areas
Remote sensing
residual attention network
Semantics
Supervision
Technical services
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Summary:Natural reserves play a leading role in safeguarding national ecological security. Remote sensing change detection (CD) technology can identify the dynamic changes of land use and warn of ecological risks in natural reserves in a timely manner, which can provide technical support for the management of natural reserves. We propose a CD method (CA-BIT) based on the improved bitemporal image transformer (BIT) model to realize the change detection of remote sensing data of Anhui Natural Reserves in 2018 and 2021. Resnet34-CA is constructed through the combination of Resnet34 and a coordinate attention mechanism to effectively extract high-level semantic features. The BIT module is also used to efficiently enhance the original semantic features. Compared with the overall accuracy of the existing deep learning-based CD methods, that of CA-BIT is 98.34% on the natural protected area CD datasets and 99.05% on LEVIR_CD. Our method can effectively satisfy the need of CD of different land categories such as construction land, farmland, and forest land.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy13030635