Recurrent Residual Deformable Conv Unit and Multi-Head with Channel Self-Attention Based on U-Net for Building Extraction from Remote Sensing Images

Abstract

Considering the challenges associated with accurately identifying building shape features and distinguishing between building and non-building features during the extraction of buildings from remote sensing images using deep learning, we propose a novel method for building extraction based on U-Net, incorporating a recurrent residual deformable convolution unit (RDCU) module and augmented multi-head self-attention (AMSA). By replacing conventional convolution modules with an RDCU, which adopts a deformable convolutional neural network within a residual network structure, the proposed method enhances the module’s capacity to learn intricate details such as building shapes. Furthermore, AMSA is introduced into the skip connection function to enhance feature expression and positions through content–position enhancement operations and content–content enhancement operations. Moreover, AMSA integrates an additional fusion channel attention mechanism to aid in identifying cross-channel feature expression Intersection over Union (IoU) score differences. For the Massachusetts dataset, the proposed method achieves an Intersection over Union (IoU) score of 89.99%, PA (Pixel Accuracy) score of 93.62%, and Recall score of 89.22%. For the WHU Satellite dataset I, the proposed method achieves an IoU score of 86.47%, PA score of 92.45%, and Recall score of 91.62%, For the INRIA dataset, the proposed method achieves an IoU score of 80.47%, PA score of 90.15%, and Recall score of 85.42%.

Keywords

• building extraction
• remote sensing
• recurrent residual convolution
• U-Net
• multi-head self-attention