Dual-Path Coding of Remote Sensing Building Image Segmentation Method

Abstract

Building segmentation in high resolution remote sensing images is one of the hotspots in remote sensing image research. The diversity of building scales in high-resolution remote sensing images easily leads to wrong segmentation, missing segmentation and fuzzy boundaries. In order to solve the above problems, this paper proposes a remote sensing building image segmentation network based on U-Net network structure with double coder U-shaped network (DCU-Net). DCU-Net adds a parallel coding path to U-Net to form a dual-path coding structure. Dense residual coding module (DRCM) and multi-scale dilated convolutional coding module (MDCCM) are designed in the encoding stage to enhance multi-scale feature extraction. The dual hybrid attention module (DFAM) is added to the network to enhance the expression ability of the network for features. In order to verify the effectiveness of the network, experiments are carried out on WHU and Massachusetts datasets. The recall, F1 and intersection over union ratio indicators reach 91.26%, 92.33% and 86.15% on WHU dataset, and reach 81.64%, 84.33% and 82.72% on Massachusetts Buildings dataset. The results show that DCU-Net has high extraction accuracy for building extraction at different scales.

Keywords

• remote sensing image; building segmentation; dual-path coding; attention mechanism; multi-scale feature