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%

Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning

“…Attention modules are introduced between the encoder and decoder to explicitly model building features. The AG module in AGSC-Net can reweight each feature map to enhance the feature response related to buildings and suppress irrelevant feature responses, thereby improving the representation capability for buildings [37]. The jump connections directly pass the features of each encoder layer to the decoder.…”