A Multiscale and Multipath Network With Boundary Enhancement for Building Footprint Extraction From Remotely Sensed Imagery

Abstract

Due to its high efficiency and low cost, automatic extraction of building footprints from remotely sensed imagery has long been an important means to obtain building footprint information, which can be easily implemented using existing fully convolutional network (FCN)-based methods. However, such methods suffer from imperfections and thus accurately extracting building footprints from remotely sensed imagery remains a challenging task. For example, cascaded convolutions generally cannot preserve the spatial details well, leading to blurred boundaries and omission of small buildings. Insufficient multiscale features fusion without considering semantic gaps between different level features could yield misclassification. In addition, the fixed receptive field always produces discontinuous holey extracted large buildings. To this end, we propose a novel multiscale and multipath network with boundary enhancement (MMB-Net) that accurately extracts building footprints from remotely sensed imagery. More specially, a parallel multipath feature extraction module is first designed to capture high spatial information-preserved multiscale features with less semantic distances. In addition, the receptive field is enlarged and broadened by a multiscale features enhancement module. Then, an attention-based multiscale features fusion module is built to appropriately aggregate multiscale features. Lastly, a spatial enhancement module is presented to refine the extracted building boundaries by capturing boundary information from low-level features. The proposed MMB-Net has been tested on two benchmark data sets together with other SOTA approaches. The results show that MMB-Net can achieve promising building footprints extraction performances and it outperforms the SOTA methods. The implementation of MMB-Net is available at.

Keywords

• Attention mechanism
• boundary enhancement
• building footprint extraction
• fully convolutional network (FCN)
• semantic gap