Research on change detection of high resolution remote sensing image based on U-type neural network

• Published in: IOP conference series. Earth and environmental science Vol. 675; no. 1; p. 12166
• Main Authors: Li, Congyi, Kong, Xiangbing, Wang, Fuling, Wang, Yinan, Zhang, Mengxuan
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2021
• Subjects: Artificial neural networks; Change detection; Environmental monitoring; High resolution; Image processing; Image resolution; Image segmentation; Land use; Medical imaging; Model testing; Neural networks; Remote sensing
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/675/1/012166

Abstract The change detection of high-resolution remote sensing images has always been a research difficulty and hot spot, which has a great application demand in land use change and ecological environment monitoring. U-type neural network can train a better model with less samples, and has good experimental effect in medical image segmentation, but it is rarely used in remote sensing image change detection research. In this paper, the U-type neural network is used to detect the change spots in GF-1 image of Yuzhou City, Henan Province, and compared with FCN, SegNet and Siamese-CNN. Experimental results show that the F1 score of U-type neural network model are 0.699 and 0.66 respectively, which are better than the other three methods, and the omission is lower, which is closer to the label map. Therefore, it is feasible to use U-type neural network for change detection of high-resolution remote sensing images, and it can have high detection accuracy.