LAUSR

The building extraction method of remote sensing images that uses deep learning algorithms can solve the problems of low efficiency and poor effect of traditional methods during feature extraction. Although some semantic segmentation networks proposed recently can achieve good segmentation performance in extracting buildings, their huge parameters and large amount of calculation lead to great obstacles in practical application. Therefore, we propose a lightweight network (named LRAD-Net) for building extraction from remote sensing images. LRAD-Net can be divided into two stages: encoding and decoding. In the encoding stage, the lightweight RegNet network with 600 million flop (600 MF) is finally selected as our feature extraction backbone net though lots of experimental comparisons. Then, a multiscale depthwise separable atrous spatial pyramid pooling structure is proposed to extract more comprehensive and important details of buildings. In the decoding stage, the squeeze-and-excitation attention mechanism is applied innovatively to redistribute the channel weights before fusing feature maps with low-level details and high-level semantics, thus can enrich the local and global information of the buildings. What's more, a lightweight residual block with polarized self-attention is proposed, it can incorporate features extracted from the space of maps and different channels with a small number of parameters, and improve the accuracy of recovering building boundary. In order to verify the effectiveness and robustness of proposed LRAD-Net, we conduct experiments on a self-annotated UAV dataset with higher resolution and three public datasets (the WHU aerial image dataset, the WHU satellite image dataset and the Inria aerial image dataset). Compared with several representative networks, LRAD-Net can extract more details of building, and has smaller number of parameters, faster computing speed, stronger generalization ability, which can improve the training speed of the network without affecting the building extraction effect and accuracy.