Title :
Road Change Detection from Multi-Spectral Aerial Data
Author :
Mancini, A. ; Frontoni, E. ; Zingaretti, P.
Author_Institution :
Univ. Politec. delle Marche, Ancona, Italy
Abstract :
The paper presents a novel approach to automate the Change Detection (CD) problem for the specific task of road extraction. Manual approaches to CD fail in terms of the time for releasing updated maps; in the contrary, automatic approaches, based on machine learning and image processing techniques, allow to update large areas in a short time with an accuracy and precision comparable to those obtained by human operators. This work is focused on the road-graph update starting from aerial, multi-spectral data. Georeferenced, ground data, acquired by a GPS and an inertial sensor, are integrated with aerial data to speed up the change detector. After roads extraction by means of a binary AdaBoost classifier, the old road-graph is updated exploiting a particle filter. In particular this filter results very useful to link (track) parts of roads not extracted by the classifier due to the presence of occlusions (e.g., shadows, trees).
Keywords :
feature extraction; image classification; learning (artificial intelligence); object detection; particle filtering (numerical methods); Global Positioning System; binary AdaBoost classifier; image processing; inertial sensor; machine learning; multispectral aerial data; occlusions; particle filter; road change detection; road extraction; road-graph; Data mining; Global Positioning System; Image resolution; Remote sensing; Roads; Satellites; Training; change detection; classification; particle filter; road extraction;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.118