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Jangir, Sandeep Kumar (2020) Aerial and Satellite Image Enhancement with Super Resolution using Deep Learning. Masterarbeit, Technical University of Munich.
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Kurzfassung
Single-Image Super Resolution (SISR) is a method to convert a low-resolution (LR) image to a high-resolution (HR) image. As the applications for aerial and satellite images arebecoming popular, there is a need for super resolution algorithms to enhance the quality the images to perform various image processing tasks such as object detection, tracking, image segmentation, and etc. Even though the basic interpolation techniques could be used to increase the spatial dimensions of an image, they are not able to reconstruct the amount of details. The aim of this thesis is to improve the SR results for aerial and satellite images using deep learning-based algorithms such as CNNs and GANs. As the resulting SR image will be further used to perform tiny lane and dense object segmentation from aerial and satellite images, thus the developed algorithm must not only enhance the quality of the images but also retrieve fine information. Training a SR algorithm requires a LR image as input and HR image as the ground-truth.The LR image is created by downsampling the HR image and since there are several ways to downsample a HR image, a comparison is made between these methods and the best method is used to create the LR images for the experiments. After creating the LR-HR image pairs for training, the proposed method is trained on aerial and satellite images respectively and their results are tabulated. Cross testing is performed between them in order to check their generic behaviours on aerial and satellite images. Even though increasing the resolution of a satellite images produce a better quality image, the perceptual quality of the resulting image still lacks the details of an aerial image. An experiment is perform to see if a satellite image can be converted into an aerial image while simultaneously increasing its spatial resolution.
| elib-URL des Eintrags: | https://elib.dlr.de/138151/ | ||||||||
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| Dokumentart: | Hochschulschrift (Masterarbeit) | ||||||||
| Zusätzliche Informationen: | This master thesis was supervised by Seyed Majid Azimi and Dr. Reza Bahmanyar. | ||||||||
| Titel: | Aerial and Satellite Image Enhancement with Super Resolution using Deep Learning | ||||||||
| Autoren: |
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| Datum: | 2020 | ||||||||
| Referierte Publikation: | Nein | ||||||||
| Open Access: | Nein | ||||||||
| Seitenanzahl: | 111 | ||||||||
| Status: | veröffentlicht | ||||||||
| Stichwörter: | Single-image Super Resolution (SISR), image enhancement, aerial and satelliteimages, convolutional neural networks, ESRGAN, SAN, interpolation | ||||||||
| Institution: | Technical University of Munich | ||||||||
| Abteilung: | Faculty of Computer Science | ||||||||
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||
| HGF - Programm: | Verkehr | ||||||||
| HGF - Programmthema: | Straßenverkehr | ||||||||
| DLR - Schwerpunkt: | Verkehr | ||||||||
| DLR - Forschungsgebiet: | V ST Straßenverkehr | ||||||||
| DLR - Teilgebiet (Projekt, Vorhaben): | V - D.MoVe (alt) | ||||||||
| Standort: | Oberpfaffenhofen | ||||||||
| Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse | ||||||||
| Hinterlegt von: | Azimi, Seyedmajid | ||||||||
| Hinterlegt am: | 27 Nov 2020 10:11 | ||||||||
| Letzte Änderung: | 01 Dez 2020 11:14 |
Verfügbare Versionen dieses Eintrags
- Aerial and Satellite Image Enhancement with Super Resolution using Deep Learning. (deposited 27 Nov 2020 10:11) [Gegenwärtig angezeigt]
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