@article{b63bb60c04dd48ee85a5962baa5679b5,
title = "SSSGAN: Satellite style and structure generative adversarial networks",
abstract = "This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area.",
keywords = "Aerial image generation, Deep learning, Generative adversarial network, High resolution image, Satellite image generation, Structure map, Style vector",
author = "Javier Mar{\'i}n and Sergio Escalera",
note = "Funding Information: This work was supported by the European Regional Development Fund (ERDF) and the Spanish Government, Ministerio de Ciencia, Innovaci?n y Universidades?Agencia Estatal de Investigaci?n?RTC2019-007434-7; and partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya), and by ICREA under the ICREA Academia programme. Funding Information: Funding: This work was supported by the European Regional Development Fund (ERDF) and the Spanish Government, Ministerio de Ciencia, Innovaci{\'o}n y Universidades—Agencia Estatal de Investigaci{\'o}n—RTC2019-007434-7; and partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya), and by ICREA under the ICREA Academia programme. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = oct,
day = "1",
doi = "10.3390/rs13193984",
language = "English",
volume = "13",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "M D P I AG",
number = "19",
}