SAU-Net: A Novel Network for Building Extraction From High-Resolution Remote Sensing Images by Reconstructing Fine-Grained Semantic Features

Abstract

The extraction of buildings from High-resolution Remote Sensing Imagery (HRSI) is crucial across various applications and stands as a pivotal task in the field of remote sensing. While recent methods based on convolutional neural networks exhibit superior performance in building extraction from HRSI, there are still challenges such as incomplete and missing extractions of buildings especially the building boundaries and the small buildings. To address these issues, we propose a Supervised Attention U-Net (SAU-Net), which combines a well-designed encoder and decoder. In the encoder, we incorporate a novel Residual Channel Attention Block (RCAB) and a Densely Connected Multidilated Convolutional Block (DMCB) to enhance semantic features in the channel and spatial dimensions, respectively. Additionally, in the decoder, we design a Supervised Attention Block (SAB) which reconstructs fine-grained semantic features by systematically refining features in a supervised way and efficiently integrating feature maps from both the encoding and decoding stages. The efficacy of SAU-Net is evaluated using four HRSI datasets encompassing varying scenarios. The experimental results highlight that SAU-Net exhibits superior performance in building extraction, particularly excelling in the extraction of building boundaries and small buildings.