| dc.description.abstract | The technology of Mobile Mapping System (MMS) is advancing rapidly. MMS can acquire image sequences and position data more efficiently and effectively. Thus, the integration of MMS and remote sensing data is an important task in geospatial technologies for various applications, such as road model reconstruction, building up three dimensional attribute databases, and so on. The primary objective of this research is to reconstruct the detailed road model and build up attribute database. Focus is placed on extracting and recognizing road markings, especially direction markings, automatically from MMS images.
The proposed scheme consists of four major parts: (1) region of interesting (ROI) extraction, (2) ROI filtering, (3) road markings recognition, (4) model integration. In the ROI extraction, the mean-shift based image segmentation is employed for automatically extracting ROI and solving the shadow area resulted from trees or cars. Secondly, in order to extract the features of ROI, Harris corner detector is utilized to detect the vertexes from ROI. Subsequently, three dimensional coordinates of vertexes from ROI are calculated using collinearity condition equations. In the third part, a proposed road markings recognition method is used to recognize ROI with road marking templates. Finally, recognized road markings and attribute information are placed to corresponding locations of Level of detail (LOD) 2 road model based on the calculated coordinates.
Experimental results demonstrate that ROI extraction is more efficient using the developed image segmentation techniques. Meanwhile the proposed algorithm can improve several ROI features of road markings recognition. In addition, the experimental results prove that using the proposed algorithms, integrating MMS data and other geo-information can reconstruct highly detailed road models with attribute database.
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