Building extraction algorithm from remote sensing images based on improved DeepLabv3+ network

Abstract

With the development of deep learning, quickly extracting high-precision building information from remote sensing images has become the research focus of intelligent application and processing of remote sensing data. Aiming at the problems of slow extraction speed and incomplete edge segmentation in building extraction in remote sensing images, a building extraction algorithm of remote sensing images based on an improved deeplabv3+ network is proposed. The more lightweight network MobileNetv3 is used to replace the original deeplabv3+ semantic segmentation model feature extraction backbone network Xception, and the standard convolution in the hole space pyramid pooling module is replaced with deep separable convolution, which reduces the amount of calculation and improves the training speed.DAMM (Dual Attention Mechanism Module) is connected in parallel with ASPP (Atous Spatial Pyramid Pooling) to improve the segmentation accuracy of edge targets. The model is verified on WHU and Massachusetts data sets. The results show that the number of training parameters and training time of the model are reduced, and the accuracy of the building extraction is effectively improved, which can meet the requirements of rapid extraction of high-precision buildings.