This repository is frozen at the state of the (fortunately successful) defence. Any follow-up of the source code happens in a dedicated repository.
Possibilities of convolutional neural networks use for remote sensing image classification
In recent years, the speed of technological progress in certain science fields is getting faster and faster. It is making it hard for other scientific areas to keep up with this tempo. One of the exemplary relationships is the link between artificial and convolutional neural network structures and the province of geomatics or remote sensing. New architectures of artificial neural network models are being published with an expeditious tempo and the common approach of the remote sensing researchers is to use the most recent structures, without the basic understanding of the background or relative performance. The goal of this thesis is to perform systematic research on the possibilities of use of chosen convolutional neural network architectures on various selected use cases from the field of remote sensing.
Ing. Ondřej Pešek
- Ing. Martin Landa, Ph.D.
- Prof. Aleš Čepek, CSc.
- Professional debate: Ing. Lukáš Brodský, Ph.D.
- The final text:
- Ing. Lucie Orlíková, Ph.D.
- doc. Ing. Martin Klimánek, Ph.D.
- Prof. Duccio Rocchini
24/10/2024
- Professional debate in the PDF version
- The final text in the PDF version (compressed - for full resolution compile the LaTeX code)
- Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types
- Convolutional Neural Networks for Urban Green Areas Semantic Segmentation on Sentinel-2 Data
- Convolutional Neural Networks for Road Surface Classification on Aerial Imagery
They more-or-less correspond to the published papers - however, some of them are enhanced to include more architectures and they are reformatted to follow the same layout. In this form, they appear in the main text.
