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In the era of abundant high-resolution aerial imagery, the automatic extraction of buildings is indispensable for applications like disaster response, environmental monitoring, and urban growth analysis. Deep learning approaches, particularly fully convolutional networks, have exhibited remarkable performance in this challenging task. Nevertheless, the accurate identification and delineation of building boundaries pose persistent challenges hindering further improvements in building extraction precision. To tackle these, we introduce a novel deep learning architecture explicitly designed for building extraction in high-resolution aerial images. Our method addresses the precise identification of building borders by combining both local and global contextual information. We efficiently preserve object boundaries and optimize the representation of straight lines within buildings through the integration of the Attentive Hough Transform and Inverse Hough Transform (AttHT-IHT) module into the U-Net architecture. Extensive experiments on the Potsdam dataset showcase substantial enhancements in building extraction accuracy, with a 97.73 % accuracy rating and a 96.42 % recall rate. Generalization capability on the WHU satellite dataset I was assessed to validate the adaptability of our proposed method.

An attentive Hough transform module for Building Extraction from High Resolution Aerial Imagery