Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

Abstract

Extracting building heights from single-view remote sensing images greatly enhances the application of remote sensing data. While methods for extracting building height from single-view shadow images have been widely studied, it remains a challenging task. The main reasons are as follows: 1) the traditional method for extracting shadow information exhibits low accuracy; and 2) the use of only shadow information to extract building height results in limited application scenarios. To solve the above problems, this article introduces building side and shadow information to complement each other, and proposes a building height extraction method from high-resolution single-view remote sensing images using shadow and side information. First, we propose the RMU-Net method, which utilizes multiscale features for the extraction of shadow and side information. This method aims to address issues related to pixel detail loss and imprecise edge segmentation, which result from significant scale differences within segmentation targets. Additionally, we employ the area threshold method to optimize the segmentation results, specifically to tackle small stray patches and holes, enhancing the overall integrity and accuracy of shadow and side information extraction. Second, we propose a method for building height extraction that integrates shadow and side information based on an enhanced proportional coefficient model. The accuracy of measuring building side and shadow lengths is improved by incorporating the fishing net method, informed by our analysis of the geometric relationships among buildings. Finally, we establish a dataset containing building shadow and side information from remote sensing images, and select multiple areas for experimental analysis. The results demonstrate a shadow extraction accuracy of 91.03% and a side extraction accuracy of 90.29%. Additionally, the average absolute error (MAE) for building height extraction is 1.22, while the average root mean square error is 1.21. Furthermore, the proposed method's validity and scalability are affirmed through experimental analyses of applicability and anti-interference performance in extensive areas.

Keywords

• Building height extraction
• deep learning
• high-resolution remote sensing images
• shadow
• side