Automatic Building Extraction from Image and LiDAR Data with Active Contour Segmentation

• Published in: Journal of the Indian Society of Remote Sensing 2018-12, Vol.46 (12), p.2057-2068
• Main Authors: Akbulut, Zeynep, Özdemir, Samed, Acar, Hayrettin, Karsli, Fevzi
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Baseline studies; Boundaries; Buildings; Completeness; Contours; Data sources; Datasets; Digitization; Disaster management; Earth and Environmental Science; Earth Sciences; Emergency preparedness; Image detection; Image segmentation; Lidar; Regularization; Remote Sensing/Photogrammetry; Research Article; Shape; Three dimensional models; Urban planning
• ISSN: 0255-660X; 0974-3006
• DOI: 10.1007/s12524-018-0871-2

Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868 , 2016 ) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy ( Q ) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.