Object detection in airborne laser scanning data - an integrative approach on object-based image and point cloud analysis

Abstract

In recent years object-based image analysis of digital elevation models acquired by airborne laser scanning gained in importance. Various applications for land cover classification (e.g. building and tree detection) already show promising results. Additionally to elevation rasters the original airborne laser scanning point cloud contains highly detailed 3D information. This paper introduces an integrative approach combining object-based image analysis and object-based point cloud analysis. This integrative concept is applied to building detection in the raster domain followed by a 3D roof facet delineation and classification in the point cloud. The building detection algorithm consists of a segmentation task, which is based on a fill sinks algorithm applied to the inverted digital surface model, and a rule-based classification task. The 340 buildings of the test site could be derived with 85% user’s accuracy and 92% producer’s accuracy. For each building object the original laser points are further investigated by a 3D segmentation (region growing) searching for planar roof patches. The finally delineated roof facets and their descriptive attributes (e.g. slope, 3D area) represent a useful input for a multitude of applications, such as positioning of solar-thermal panels and photovoltaics or snow load capacity modeling.