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Main Title: An Approach To Super-Resolution Of Sentinel-2 Images Based On Generative Adversarial Networks
Author(s): Zhang, Kexin
Sumbul, Gencer
Demir, Begüm
Type: Conference Object
Language Code: en
Abstract: This paper presents a generative adversarial network based super-resolution (SR) approach (which is called as S2GAN) to enhance the spatial resolution of Sentinel-2 spectral bands. The proposed approach consists of two main steps. The first step aims to increase the spatial resolution of the bands with 20m and 60m spatial resolutions by the scaling factors of 2 and 6, respectively. To this end, we introduce a generator network that performs SR on the lower resolution bands with the guidance of the bands associated to 10m spatial resolution by utilizing the convolutional layers with residual connections and a long skip-connection between inputs and outputs. The second step aims to distinguish SR bands from their ground truth bands. This is achieved by the proposed discriminator network, which alternately characterizes the high level features of the two sets of bands and applying binary classification on the extracted features. Then, we formulate the adversarial learning of the generator and discriminator networks as a min-max game. In this learning procedure, the generator aims to produce realistic SR bands as much as possible so that the discriminator incorrectly classifies SR bands. Experimental results obtained on different Sentinel-2 images show the effectiveness of the proposed approach compared to both conventional and deep learning based SR approaches.
Issue Date: 2-Jun-2020
Date Available: 24-Nov-2020
DDC Class: 006 Spezielle Computerverfahren
Subject(s): Sentinel-2 images
generative adversarial network
remote sensing
Sponsor/Funder: EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarth
Proceedings Title: 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Publisher DOI: 10.1109/M2GARSS47143.2020.9105165
ISBN: 978-1-7281-2190-1
Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

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