Regularizing building outlines extracted from remote sensing images by integrating multiple algorithms

Abstract

Extracting building from remote sensing images is crucial, but the extracted outlines still face issues such as point redundancy and lack of right-angle features, relying on further regularization. This study proposed an integrated regularization method to combine the strengths of multiple algorithms. 22 metrics were calculated to describe the geometric characteristic of outlines, followed by principal component analysis for selecting key metrics. Further, a supervised learning model was constructed to analyze these key metrics and determine the most suitable algorithm among three candidates–rectangle transformation, recursive regression, and feature edge reconstruction–for regularizing each building. Experimental results demonstrated that our method can adaptively select the appropriate algorithm based on the metrics, achieving regularization results superior to those obtained by using algorithms independently. Compared with two existing geometric correction-based methods, our method excels in preserving the orientation, area, and shape. Our method also has advantages over learning-based methods in maintaining the orthogonality.

Keywords

• Building mapping
• building outline regularization
• machine learning
• remote sensing image