نوع مقاله: مقاله پژوهشی
نویسندگان
1 دانشجوی دکترای مهندسی نقشه برداری، دانشکده مهندسی ژئوماتیک، دانشگاه صنعتی خواجه نصیرالدین طوسی
2 استادیار گروه فتوگرامتری و سنجش از دور، دانشکده مهندسی ژئوماتیک، دانشگاه صنعتی خواجه نصیرالدین طوسی
3 استاد گروه فتوگرامتری و سنجش از دور، دانشکده مهندسی ژئوماتیک، دانشگاه صنعتی خواجه نصیرالدین طوسی
چکیده
کلیدواژه‌ها
عنوان مقاله [English]
نویسندگان [English]
Abstract
3D point clouds obtained by Airborne Laser Scanner Systems provide a varied and unique geometric information of the physical terrain surfaces due to advantages such as relatively high geometric accuracy and high spatial density of the points. Classification and separation of cloud point data to environmental constructive terrains plays an important role in the process of 3D modeling of terrains. In this procedure, point cloud clustering is a fundamental step in the procedure of information extraction form LiDAR's data. In this paper, a novel method is proposed for supervised classification of LiDAR cloud of points based on contextual analysis of LiDAR points. The proposed method consists of three main steps. In the first step, a set of features based on contextual analyses are produced for each point in LiDAR data. In the second step, the optimum feature selection is done in the modified prototype space using a new strategy. The last step is conducted by a simple k-means clustering in the feature space spanned by optimum contextual clusters. An urban area with the residential texture has been used as the case study to evaluate the proposed method. The results indicate proper classification accuracies. The overall accuracies and kappa coefficients were 93.15% and 0.89 respectively.
کلیدواژه‌ها [English]
1- مجردی، برات، 1388. استخراج ویژگی به منظور طبقه‌بندی تصاویر ابرطیفی، رساله دکتری، دانشکده ژئودزی و ژئوماتیک، دانشگاه صنعتی خواجه نصیرالدین طوسی.
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