LFEMAP-Net: Low-Level Feature Enhancement and Multiscale Attention Pyramid Aggregation Network for Building Extraction From High-Resolution Remote Sensing Images

With the rapid development of Earth observation technology and deep learning, building extraction from remotely sensed imagery based on deep convolutional neural networks has attracted wide attention in recent years. However, due to the heterogeneity of building shapes and sizes and the complexity of the surrounding objects, current building extraction methods still have challenges in boundary accuracy and complete building extraction. For these purposes, we proposed a low-level feature enhancement and multiscale attention pyramid aggregation network (LFEMAP-Net) that considers building boundary information and multiscale feature expression to obtain higher accuracy building extraction. First, a low-level feature enhancement model is proposed based on the prior edge information to enhance the representation of spatial details, effectively addressing issues related to information loss and fuzzy boundaries. Additionally, a multiscale attention pyramid aggregation model is developed during the decoding stage to facilitate the fusion of features from different scales, thereby enhancing the extraction of building features. The experimental results on two publicly available datasets validate that LFEMAP-Net can overcome building extraction interruptions and boundary blur in complex scenes, and achieve boundary optimization and complete segmentation of buildings and achieve better performance than other advanced semantic segmentation models.