Building Extraction Algorithm for Remote Sensing Images by Fusing Partial Convolution and Residual Refinement

Due to the high similarity between background and buildings in high spatial resolution remote sensing images, which makes it difficult for the network to take into account buildings of different sizes, the pixels in the building boundary region are confused with the background, and the building boundaries are easily missed. In order to solve the above problems, the building extraction algorithm (UUNet) for remote sensing images fusing partial convolution and residual refinement is proposed. Using U-Net as the baseline network, firstly, this paper improves the encoder. It adds two Conv4×4 at the front end of the encoder to expand the sensing field at the beginning and capture more remote sensing image feature information. It utilizes the PC module constructed by partial convolution (PConv3×3) to enhance the ability of the encoder to extract multi-scale building features, and downsamples twice with Conv2×2 to reduce the loss of building feature information. Secondly, this paper reduces the number of parameters. It crops the three-layer structure of the U-Net network decoder to a UUNet network decoder. Lastly, it adds an improved residual refinement module. It constructs a U-shaped residual refinement module cropped to a three-layer structure at the output of the decoder, to further purify the rough building feature maps output from the decoder, so as to make the edge information of the buildings clearer. Decoder is jump-connected to the encoder of the U-shaped residual refinement module to preserve the initial features, and SimAM is embedded in the refinement module to improve the building focus, optimize the network to improve the boundary blurring, and enhance the quality of target boundary extraction. In the ablation experiment conducted on the Satellite dataset II (East Asia), UUNet shows improvements over U-Net, with IoUBuilding, IoUBackground, F1, OA and mIoU increased by 2.78 percentage points, 0.12 percentage points, 1.91 percentage points, 0.19 percentage points and 1.45 percentage points, respectively, indicating that UUNet outperforms the baseline network. Furthermore, comparative experiments on both Satellite dataset II (East Asia) and WHU dataset demonstrate that UUNet performs better than existing mainstream algorithms, significantly enhancing building extraction in high-resolution remote sensing images.