Automatic Building Extraction via Adaptive Iterative Segmentation With LiDAR Data and High Spatial Resolution Imagery Fusion

Abstract

Extracting buildings from remotely sensed data is a fundamental task in many geospatial applications. However, this task is resistant to automation due to variability in building shapes and the environmental complexity surrounding buildings. To solve this problem, this article introduces a novel automatic building extraction method that integrates LiDAR data and high spatial resolution imagery using adaptive iterative segmentation and hierarchical overlay analysis based on data fusion. An adaptive iterative segmentation method overcomes over- and undersegmentation based on the globalized probability of boundary contour detection algorithm. A data-fusion-based hierarchical overlay analysis extracts building candidate regions based on segmentation results. A morphological operation optimizes a building candidate region to obtain final building results. Experiments were conducted on the international society for photogrammetry and remote sensing (ISPRS) Vaihingen benchmark dataset. The extracted building footprints were compared with those extracted using the state-of-the-art methods. Evaluation results show that the proposed method achieved the highest area-based quality compared to results from the other tested methods on the ISPRS website. A detailed comparison with four state-of-the-art methods shows that the proposed method requiring no samples achieves competitive extraction results. Furthermore, the proposed method achieved a completeness of 94.1%, a correctness of 90.3%, and a quality of 85.5% over the whole Vaihingen dataset, indicating that the method is robust, with great potential in practical applications.

Keywords

• Adaptive segmentation
• building extraction
• data fusion
• high spatial resolution imagery (HSRI)
• LiDAR