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

Abstract

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.

New Trends and Challenges in Precision and Digital Agriculture

“…More and more producers are successfully applying information technology to their farms, so the concept of digital agriculture is gaining importance. This solution focuses the automation of machinery and processes, involving the latest developments in artificial intelligence: classical and convolutional neural networks [1,[6][7][8][9][10]; analysis of diverse images [8,[11][12][13][14]; cloud computing and unmanned aerial vehicles [15][16][17][18][19], etc. Digital technologies in agriculture enable a better understanding of the interdependence of factors that determine various aspects of the business.…”