OBJECT EXTRACTION AND RECOGNITION FROM LIDAR DATA BASED ON FUZZY REASONING AND INFORMATION FUSION TECHNIQUES
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AbstractThree dimensional object extraction and recognition (OER) from LIDAR data has been an area of major interest in photogrammetry for quite a long time. However, most of the existing methods for automatic object extraction and recognition from LIDAR data are just based on the range information and employ parametric methods and object’s vagueness behaviour is basically neglected. Thus, these methods do not take into account the extraction and recognition complexities and may fail to reach a satisfied reliability level in complex situations. In this paper a novel approach based on the following strategies is formulated and implemented: (a) for a more comprehensive definition of the objects, information fusion concept is utilized, i.e., object’s descriptive components such as 3D structural and textural (ST) information are automatically extracted from first/last rang and intensity information of LIDAR data and simultaneously fed into the evaluation process, (b) for a more realistic expression of the objects and also for simultaneous fusion of the extracted ST components, the fuzzy reasoning strategy is employed. The proposed automatic OER methodology is evaluated for two different object classes of buildings and trees, using a portion of LIDAR data of an urban area. The visual inspection of the recognized objects demonstrates promising results. 1.