Dissertation/ Thesis
From satellite images to vector maps ; Des images satellites aux cartes vectorielles
| العنوان: | From satellite images to vector maps ; Des images satellites aux cartes vectorielles |
|---|---|
| المؤلفون: | Tasar, Onur |
| المساهمون: | Geometric Modeling of 3D Environments (TITANE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur, Pierre Alliez |
| المصدر: | https://theses.hal.science/tel-02989681 ; Image Processing [eess.IV]. Université Côte d'Azur, 2020. English. ⟨NNT : 2020COAZ4063⟩. |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2020 |
| المجموعة: | HAL Université Côte d'Azur |
| مصطلحات موضوعية: | Deep learning, Semantic segmentation, Satellite images, Domain adaptation, Incremental learning, Image vectorization, Apprentissage profond, Segmentation sémantique, Images satellites, Adaptation de domaine, Apprentissage incrémental, Vectorisation d’images, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] |
| الوصف: | With the help of significant technological developments over the years, it has been possible to collect massive amounts of remote sensing data. For example, the constellations of various satellites are able to capture large amounts of remote sensing images with high spatial resolution as well as rich spectral information over the globe. The availability of such huge volume of data has opened the door to numerous applications and raised many challenges. Among these challenges, automatically generating accurate maps has become one of the most interesting and long-standing problems, since it is a crucial process for a wide range of applications in domains such as urban monitoring and management, precise agriculture, autonomous driving, and navigation.This thesis seeks for developing novel approaches to generate vector maps from remote sensing images. To this end, we split the task into two sub-stages. The former stage consists in generating raster maps from remote sensing images by performing pixel-wise classification using advanced deep learning techniques. The latter stage aims at converting raster maps to vector ones by leveraging computational geometry approaches. This thesis addresses the challenges that are commonly encountered within both stages. Although previous research has shown that convolutional neural networks (CNNs) are able to generate excellent maps when training data are representative for test data, their performance significantly drops when there exists a large distribution difference between training and test images. In the first stage of our pipeline, we mainly aim at overcoming limited generalization abilities of CNNs to perform large-scale classification. We also explore a way of leveraging multiple data sets collected at different times with annotations for separate classes to train CNNs that can generate maps for all the classes.In the second part, we propose a method that vectorizes raster maps to integrate them into geographic information systems applications, which completes our ... |
| نوع الوثيقة: | doctoral or postdoctoral thesis |
| اللغة: | English |
| Relation: | NNT: 2020COAZ4063 |
| الاتاحة: | https://theses.hal.science/tel-02989681 https://theses.hal.science/tel-02989681v2/document https://theses.hal.science/tel-02989681v2/file/2020COAZ4063.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.99AAE3E7 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |