Öǧrenme Transferi Temelli Hava Araci Görüntülerinin Süper Çözünürlüǧü

Translated title of the contribution: Transfer Learning Based Super Resolution of Aerial Images

Aslan Ahmet Haykir, Ilkay Oksuz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Images created using the Super Resolution method can generate more information compared to their low resolution counterparts. A super-resolved image, which is created using an original image captured by an imaging source is not only more meaningful to human perception but also has advantages on downstream tasks such as object detection and pattern recognition. In this work, we aim to apply the Super Resolution method to the Aerial Images captured for surveillance to enable more information about the original scenes. To achieve this Super Resolution Generative Adversarial Network (SRGAN), which is based on the Generative Adversarial Networks architecture is used. We also applied transfer learning methodology to achieve better image quality. Public xView and DOTA datasets which contain images mostly captured by satellites around the world are used to train a generative model via SRGAN architecture. Furthermore, DIV2K dataset is used to pre-train a generative model, and then the transfer learning technique is used to train separate models on xView and DOTA validation datasets. Perceptual Index (PI) and Root Mean Squared Error (RMSE) which are used on European Conference on Computer Vision -Perceptual Image Restoration and Manipulation Workshop 2018 are computed as the performance metrics. We have seen that the model which gives the best PI results, i.e. better perceptual quality, on xView and DOTA validation datasets is the one trained using the DIV2K dataset and the model which gives the best RMSE results, i.e. better reconstruction quality, is the one trained using the transfer learning technique.

Translated title of the contributionTransfer Learning Based Super Resolution of Aerial Images
Original languageTurkish
Title of host publication2022 30th Signal Processing and Communications Applications Conference, SIU 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665450928
DOIs
Publication statusPublished - 2022
Event30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey
Duration: 15 May 202218 May 2022

Publication series

Name2022 30th Signal Processing and Communications Applications Conference, SIU 2022

Conference

Conference30th Signal Processing and Communications Applications Conference, SIU 2022
Country/TerritoryTurkey
CitySafranbolu
Period15/05/2218/05/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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