AGDF-Net: Attention-Gated and Direction-Field-Optimized Building Instance Extraction Network

Abstract

Building extraction from high-resolution remote sensing images has various applications, such as urban planning and population estimation. However, buildings have intraclass heterogeneity and interclass homogeneity in high-resolution remote sensing images with complex backgrounds, which makes the accurate extraction of building instances challenging and regular building boundaries difficult to maintain. In this paper, an attention-gated and direction-field-optimized building instance extraction network (AGDF-Net) is proposed. Two refinements are presented, including an Attention-Gated Feature Pyramid Network (AG-FPN) and a Direction Field Optimization Module (DFOM), which are used to improve information flow and optimize the mask, respectively. The AG-FPN promotes complementary semantic and detail information by measuring information importance to control the addition of low-level and high-level features. The DFOM predicts the pixel-level direction field of each instance and iteratively corrects the direction field based on the initial segmentation. Experimental results show that the proposed method outperforms the six state-of-the-art instance segmentation methods and three semantic segmentation methods. Specifically, AGDF-Net improves the objective-level metric AP and the pixel-level metric IoU by 1.1%~9.4% and 3.55%~5.06%