Abstract: In response to the problems of low segmentation accuracy and blurred segmentation boundaries in traditional methods for extracting buildings from high-resolution remote sensing images, this paper proposes a semantic segmentation method for building features based on the U-Net3+ model. Firstly, on the basis of the U-Net network structure, the feature maps of different scales are fused using full-scale jump connection.Then, the feature expressions are learned from the multi-scale aggregated feature maps by deep supervision, and the cross-entropy loss function is used for training.Finally, different model parameters are tuned and tested according to the dataset characteristics to achieve the best segmentation effect. Experimental results show that the image segmentation accuracy and feature edge segmentation completeness based on the U-Net3+ model significantly improve in comparison with the U-Net and U-Net++ models, and the highest accuracy is achieved when setting epoch as 15 in all three models. Based on the U-Net3+ model, the segmentation accuracy of building features for high-resolution remotesensing images reaches 96.62% and the average intersection ratio of mIoU reaches 0.902 7, which reduces the phenomenon of missegmentation and omission, and reduces the model parameters, the model loss convergence rate is fast and the training period is shortened, and the extraction accuracy of buildings is significantly improved.

Key words: feature information extraction, U-Net3+ model, full-scale jump connectivity, deep supervision, accuracy