Localisation d'objets urbains à partir de sources multiples dont des images aériennes

Lionel Pibre 1
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This thesis addresses problems related to the location and recognition of urban objects in multi-source images (optical, infrared, terrain model) of very high precision acquired by air.Urban objects (lamp posts, poles, car, tree...) have dimensions, shapes, textures and very variable colors. They can be glued to each other and are small with respect to the size of an image. They are present in large numbers but can be partially hidden. All this makes urban objects difficult to identify with current image processing techniques.First, we compared traditional learning approaches, consisting of two stages - extracting features through a predefined descriptor and using a classifier - to deep learning approaches and more precisely Convolutional Neural Networks (CNN). CNNs give better results but their performances are not sufficient for industrial use. We therefore proposed two contributions to increase performance.The first is to efficiently combine data from different sources. We compared a naive approach that considers all sources as components of a multidimensional image to an approach that merges information within CNN itself. For this, we have processed the different information in separate branches of the CNN.For our second contribution, we focused on the problem of incomplete data. Until then, we considered that we had access to all the sources for each image but we can also place ourselves in the case where a source is not available or usable. We have proposed an architecture to take into account all the data, even when a source is missing in one or more images. We evaluated our architecture and showed that on an enrichment scenario, it allows to have a gain of more than 2% on the F-measure.The proposed methods were tested on a public database. They aim to be integrated into a Berger-Levrault company software in order to enrich geographic databases and thus facilitate the management of the territory by local authorities.
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Lionel Pibre. Localisation d'objets urbains à partir de sources multiples dont des images aériennes. Autre [cs.OH]. Université Montpellier, 2018. Français. ⟨NNT : 2018MONTS107⟩. ⟨tel-02156868⟩

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