Learning Approaches for Large-Scale Remote Sensing Image Classification

Emmanuel Maggiori 1
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The analysis of airborne and satellite images is one of the core subjects in remote sensing. In recent years, technological developments have facilitated the availability of large-scale sources of data, which cover significant extents of the earth’s surface, often at impressive spatial resolutions. In addition to the evident computational complexity issues that arise, one of the current challenges is to handle the variability in the appearance of the objects across different geographic regions. For this, it is necessary to design classification methods that go beyond the analysis of individual pixel spectra, introducing higher-level contextual information in the process. In this thesis, we first propose a method to perform classification with shape priors, based on the optimization of a hierarchical subdivision data structure. We then delve into the use of the increasingly popular convolutional neural networks (CNNs) to learn deep hierarchical contextual features. We investigate CNNs from multiple angles, in order to address the different points required to adapt them to our problem. Among other subjects, we propose different solutions to output high-resolution classification maps and we study the acquisition of training data. We also created a dataset of aerial images over dissimilar locations, and assess the generalization capabilities of CNNs. Finally, we propose a technique to polygonize the output classification maps, so as to integrate them into operational geographic information systems, thus completing the typical processing pipeline observed in a wide number of applications. Throughout this thesis, we experiment on hyperspectral, satellite and aerial images, with scalability, generalization and applicability goals in mind.
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Submitted on : Monday, September 18, 2017 - 6:51:12 PM
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Emmanuel Maggiori. Learning Approaches for Large-Scale Remote Sensing Image Classification. Computer Vision and Pattern Recognition [cs.CV]. Université Côte d'Azur, 2017. English. ⟨tel-01589661v1⟩

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