Abstract
The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object’s size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m2), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts.
Highlights
The automated extraction and localisation of urban objects is an active field of research in photogrammetry with the focus on detailed representations
The detailed quality measures for building delineation technique before and after the regularisation can be found on the ISPRS portal [43] under detection with acronym
FED_2 performed significantly better on the Australian data sets which are characterised more complex than the ISPRS benchmark due to dense vegetation, shadows, and topography
Summary
The automated extraction and localisation of urban objects is an active field of research in photogrammetry with the focus on detailed representations. Building being a key urban object is indispensable to diverse applications in cartographic mapping, urban planning, civilian and military emergency responses, and the crisis management [1,2]. Accurate and updated information of the Remote Sens. 2016, 8, 258 buildings is quite imperative to keep these applications operational. A building may appear to be a simple object that can be classified and extracted. In reality, automatic building extraction is quite challenging due to the differences in viewpoint, surrounding environment, and complex shape and size of the buildings. There have been several attempts to develop a fully autonomous system that can deal with occlusion, shadow, and vegetation and identify objects with different geometric and radiometric characteristics from diverse and complex scenes
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