Affiliations: [a]
School of Electrical and Electronic Engineering Hubei University of Technology, Wuhan, China
| [b]
Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy Hubei University of Technology, Wuhan, China
Correspondence:
[*]
Corresponding author. Yingying Dai, School of Electrical and Electronic Engineering Hubei University of Technology, Wuhan, 430068 China. E-mail: [email protected].
Abstract: Remote sensing image change detection is to analyze the change information of two images from the same area at different times. It has wide applications in urban expansion, forest detection, and natural disaster. In this paper, Feature Fusion Network is proposed to solve the problems of slow change detection speed and low accuracy. The MobileNetV3 block is adopted to efficiently extract features and a self-attention module is applied to investigate the relationship between heterogeneous feature maps (image features and concatenated features). The method is tested in data sets SZTAKI and LEVIR-CD. With 98.43 percentage correct classification, it is better than other comparative networks, and its space complexity is reduced by about 50%. The experimental results show that it has better performance and can improve the accuracy or speed of change detection.
