Language: | Slovenian |
---|---|
Year of publishing: | 2020 |
Typology: | 2.11 - Undergraduate Thesis |
Organization: | UL FGG - Faculty of Civil and Geodetic Engineering |
Publisher: | [M. Krebs] |
UDC: | 004.6:528.7(043.2) |
COBISS: | 46036739 |
Views: | 470 |
Downloads: | 138 |
Average score: | 0 (0 votes) |
Metadata: |
Secondary language: | English |
---|---|
Secondary title: | ǂThe ǂ improving data resolution of Sentinel-2 |
Secondary abstract: | The purpose of this thesis is resolution enhancement of Sentinel-2 data. The thesis tests a super resolution method based on machine learning of two convolutional neural networks. Sentinel-2 data are given in spatial resolutions of 10 m, 20 m and 60 m, depending on the individual spectral channels. The aim of the thesis is to obtain the complete dataset in 10 m. Web application Google Colab and the basics machine learning focused on neural networks are described. A convolutional neural network called DSen2 is presented. The network has been enhanced with eight different Sentinel-2 images of the area of Slovenia. Describing the selection of images, and the process of resolution enhancement. At the end of the thesis a comparison of the results and the original data are described. |
Secondary keywords: | graduation thesis;geodesy;Sentinel-2;resolution enhancment;DSen2;super-resolution;Google Colab; |
Type (COBISS): | Bachelor thesis/paper |
Study programme: | 0 |
Embargo end date (OpenAIRE): | 1970-01-01 |
Thesis comment: | Univ. v Ljubljani, Fak. za gradbeništvo in geodezijo |
Pages: | XX, 30 str. |
ID: | 12046415 |