CLASSIFICATION OF POLE-LIKE OBJECTS USING POINT CLOUDS AND IMAGES CAPTURED BY MOBILE MAPPING SYSTEMS
| Autor: | Y. Mori, K. Kohira, H. Masuda |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2, Pp 731-738 (2018) |
| Druh dokumentu: | article |
| ISSN: | 1682-1750 2194-9034 |
| DOI: | 10.5194/isprs-archives-XLII-2-731-2018 |
| Popis: | The vehicle-based mobile mapping system (MMS) is effective for capturing 3D shapes and images of roadside objects. The laser scanner and cameras on the MMS capture point-clouds and sequential digital images synchronously during driving. In this paper, we propose a method for detecting and classifying pole-like objects using both point-clouds and images captured using the MMS. In our method, pole-like objects are detected from point-clouds, and then target objects, which are objects attached to poles, are extracted for identifying the types of pole-like objects. For associating each target object with images, the points of the target object are projected onto images, and the image of the target object is cropped. Each pole-like object is represented as a feature vector, which are calculated from point-clouds and images. The feature values of a point-cloud are calculated by point processing, and the ones of the cropped image are calculated using a convolutional neural network. The feature values of point-clouds and images are unified, and they are used as the input to machine learning. In experiments, we classified pole-like objects using three methods. The first method used only point-clouds, the second used only images, and the third used both point-clouds and images. The experimental results showed that the third method could most accurately classify pole-like objects. |
| Databáze: | Directory of Open Access Journals |
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