A Hybrid Pyramid and Strip Pooling Network for Accurate Building Extraction from Remote Sensing Images
TL;DR
SRB-Net is presented, a U-Net-based framework that combines three complementary components: strip pooling for long-range horizontal and vertical context; residual multi-scale atrous spatial pyramid pooling with squeeze-and-excitation blocks for multi-scale and channel-aware feature learning; and a bottleneck attention module (BAM) for refining skip-connection features.
Abstract
Accurate extraction of building footprints from remote sensing imagery is important for urban planning, disaster management, and geographic information systems. However, complex building shapes, occlusions, and scale variation continue to challenge conventional segmentation models. This paper presents SRB-Net, a U-Net-based framework that combines three complementary components: (1) strip pooling (SP) for long-range horizontal and vertical context; (2) residual multi-scale atrous spatial pyramid pooling (RMASPP) with squeeze-and-excitation (SE) blocks for multi-scale and channel-aware feature learning; and (3) a bottleneck attention module (BAM) for refining skip-connection features. The model was trained with the Adam optimizer and evaluated on the aerial and Satellite Dataset II subsets of the WHU Building Dataset. Among the evaluated baselines, SRB-Net achieved the best overall performance, reaching 98.83% accuracy and 90.12% Intersection over Union (IoU) on the aerial dataset and 98.28% accuracy and 70.89% IoU on Satellite Dataset II. These results show consistent performance improvements across the two evaluated WHU subsets while avoiding claims beyond the within-dataset experimental setting.