Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 53-56, 2013
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W1/53/2013/
doi:10.5194/isprsarchives-XL-1-W1-53-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
30 Apr 2013
ROAD REGION DETECTION IN URBAN AREAS COMBINING HIGH-RESOLUTION RGB IMAGE AND LASER SCANNING DATA IN A CLASSIFICATION FRAMEWORK
A. P. Dal Poz and T. S. G. Mendes Dept. of Cartography, São Paulo State University, R. Roberto Simonsen, 305, Presidente Prudente-SP, Brazil
Keywords: Artificial Neural Network, RGB Aerial Image, Normalized Digital Surface Model, Laser Pulse Intensity Image Abstract. This paper addresses the problem of road region detection in urban areas using an image classification approach. In order to minimize the spectral superposition of the road (asphalt) class with other classes, the Artificial Neural Networks (ANN) image classification method was used to classify geometrically-integrated high-resolution RGB aerial and laser-derived images. The RGB image was combined with different laser data layers and the ANN classification results showed that the radiometric and geometric laser data allows a better detection of road pixel.
Conference paper (PDF, 372 KB)


Citation: Dal Poz, A. P. and Mendes, T. S. G.: ROAD REGION DETECTION IN URBAN AREAS COMBINING HIGH-RESOLUTION RGB IMAGE AND LASER SCANNING DATA IN A CLASSIFICATION FRAMEWORK, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 53-56, doi:10.5194/isprsarchives-XL-1-W1-53-2013, 2013.

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