Research on Extraction of Building Change Information Based on an Improved U-Net Model
The extraction of building change information is one of the important contents of remote sensing image extraction,which is of great significance to land survey,urban planning and land law enforcement. Aiming at the problems of poor prediction effect and omis-sion detection in the original U-Net model,this paper proposes an improved U-Net model which integrates the aggregated residual convolution block and attention module. Compared with the original U-Net model,the accuracy of the improved U-Net model in building change information extraction is greatly improved. This study can provide some technical support for building change monito-ring.
deep learningbuilding extractionaggregated residual convolution blockattention mechanism
万方数据
维普
