Building Extraction of Multi source Data Based on Deep Learning
Aiming at the poor robustness of high resolution remote sensing image building extraction methods based on machine learning and it is difficult to fully exploit the deep features in images, several methods based on deep learning semantic segmentation have been compared and fully analyzed with average precision, category accuracy, F1 score and IoU as measures. The results show that DeepLabv3plus model can obtain more accurate pixel level building extraction results, the overall accuracy can be improved by 2% IoU can be increased by 3% and the boundary details can be fully expressed. A method of automatic labeling is designed based on multi source data. Experiments were carried out and proved that the sample sets produced by our method is close to the training results of true value labels.
2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)