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Modélisation, détection et classification d'objets urbains à partir d’images photographiques aériennes

Jérôme Pasquet 1 
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This thesis deals with the problems of automatic localization and recognition of urban objects in high-definition aerial images. Urban object detection is a challenging problem because they vary in appearance, color and size. Moreover, there are many urban objects which can be very close to each other in an image. The localization and the automatic recognition of different urban objects, considering these characteristics, are very difficult to detect and classical image processing algorithms do not lead to good performances. We propose then to use the supervised learning approach. In a first time, we have built a Support Vector Machine (SVM) network to merge different resolutions in an efficient way. However, this method highly increases the computational cost. We then proposed to use an “activation path” which reduces the complexity without any loss of efficiency. This path activates sequentially the network and stops the exploration when an urban object has a high probability of detection. In the case of localizations based on a feature extraction step followed by a classification step, this may reduce by a factor 5 the computational cost. Thereafter, we show that we can combine an SVM network with feature maps which have been extracted by a Convolutional Neural Network. Such an architecture associated with the activation path increased the performance by 8% on our database while giving a theoretical reduction of the computational costs up to 97%. We implemented all these new methods in order to be integrated in the software framework of Berger-Levrault company, to improve land registry for local communities.
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Jérôme Pasquet. Modélisation, détection et classification d'objets urbains à partir d’images photographiques aériennes. Informatique. Université Montpellier, 2016. Français. ⟨NNT : 2016MONTT283⟩. ⟨tel-01808884⟩



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