Remote sensing image change detection is to use multi-temporal images to determine the changes of objects or phenomena within a certain period of time, and to provide qualitative and quantitative information on spatial changes of objects. Traditional remote sensing image change detection methods are mainly based on ground texture and spatial features, which is difficult to accurately identify new buildings in remote sensing images. Therefore, this paper adopts a building change detection method based on UNet network. Firstly, the lightweight efficient channel attention network (ECANet) is injected into the original UNet network model to adjust and optimize the network structure and improve the accuracy of image segmentation. The parameters of SENet are then tuned to enhance the accuracy of building change detection in remote sensing images. Experiments on a high-resolution dataset LIVER-CD show that the accuracy of the proposed method reaches a semantic segmentation accuracy of 99.03% and a building change detection accuracy of 98.62%. Compared with other methods, the proposed method can effectively enhance the effective features of images and improve the detection accuracy of ground buildings in remote sensing images.
YIN Meijie, NI Cui, WANG Peng, ZHANG Guangyuan
. Building Change Detection in Remote Sensing Images Based on Semantic Segmentation[J]. Journal of Applied Sciences, 2023
, 41(3)
: 448
-460
.
DOI: 10.3969/j.issn.0255-8297.2023.03.007
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