A Review of Urban Building Extraction From Synthetic Aperture Radar Imagery Based on Deep Learning

Urban building extraction from Synthetic Aperture Radar (SAR) imagery is a hot topic and remains challenging for wide applications. Recently, deep learning approaches, which have achieved remarkable success in computer vision, are increasingly becoming a major trend in SAR image urban building extraction. However, there remains a lack of systematic reviews on urban building extraction from SAR imagery based on deep learning. This article aims to present a comprehensive review of methodologies in this field since 2015. It includes detailed analyses and discussions of approaches, datasets, and evaluation metrics. The fundamental imaging principles of urban buildings in SAR imagery are first explored to clarify existing challenges. Then, the review is conducted from two aspects: urban building extraction and building height estimation. The methods are further classified as only-SAR image-based methods and SAR-optical data-based methods, depending on the data sources. Additionally, given the distinct feature representations across SAR resolutions, investigations are differentiated between building-level analysis for high-resolution images and built-up area level analysis for medium-resolution data. Benchmark datasets are summarized, and evaluation metrics are reviewed. Finally, the article conducts discussions, proposes potential solutions, and outlines future development directions.