Multi-scale attention integrated hierarchical networks for high-resolution building footprint extraction

Abstract

Convolutional neural networks show excellent performance in image segmentation. However, compared with natural images, remote sensing images are characterized by large coverage, multi-scale nesting, and complex geographic context. Therefore, it has been a challenging task to extract the building footprint from high-resolution remote sensing images. In this study, an end-to-end Multi-Scale Geoscience Network (MS-GeoNet) is proposed for building footprint extraction. The proposed architecture focuses on multi-scale nested characteristics and the spatial correlation between buildings and surroundings. The performance of a number of embedding modules and loss functions in extracting various types of buildings are explored in detail. Our proposed method outperforms the baseline model Fully Convolutional DenseNets (FC- DenseNet) by 7.10% for the intersection over union (IoU) and by 3.09% for F1-score. Moreover, to increase the accuracy of large area interpretation, an overlap splicing and voting mechanism is proposed. It is also an effective means to solve the edge processing task. The proposed method demonstrates approximately 1.19% IoU improvement and 0.83% F1-score improvement on our dataset, compared with the traditional splicing method. MS-GeoNet is a promising approach for automatic generation of building footprint in practical applications.