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Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusionby highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborneLiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDARdata is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as theprimary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildingsand trees), while the white region describes the ground (earth). The second phase begins with the process of Connected ComponentAnalysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling.Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unitdistance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to aneighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents aprospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clusteringprocess clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step,NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building’s occluded parts.Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineatethe building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australiandata sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed techniquedetects buildings larger than 50 m2 and 10 m2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the HerveyBay site it performs better with 100% accuracy for buildings larger than 10 m2 in area

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

FUSION OF LIDAR DATA AND MULTISPECTRAL IMAGERY FOR EFFECTIVE BUILDING DETECTION BASED ON GRAPH AND CONNECTED COMPONENT ANALYSIS