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  This paper presents an automatic building extraction approach using LiDAR data  and aerial photographs from a multi-sensor system positioned at the same platform.  The automatic building extraction approach consists of segmentation, analysis and  classification steps based on object-based image analysis. The chessboard, contrast  split and multi-resolution segmentation methods were used in the segmentation step.  The determined object primitives in segmentation, such as scale parameter, shape,  completeness, brightness, and statistical parameters, were used to determine  threshold values for classification in the analysis step. The rule-based classification  was carried out with defined decision rules based on determined object primitives  and fuzzy rules. In this  study, hierarchical classication was preferred. First, the  vegetation and ground classes were generated; the building class was then extracted.  The NDVI, slope and Hough images were generated and used to avoid confusing  the building class with other classes. The intensity images generated from the  LiDAR data and morphological operations were utilized to improve the accuracy of  the building class. The proposed approach achieved an overall accuracy of  approximately 93% for the target class in a suburban neighborhood, which was the  study area. Moreover, completeness (96.73%) and correctness (95.02%) analyses  were performed by comparing the automatically extracted buildings and reference  data.

Automatic building extraction using LiDAR and aerial photographs