Please use this identifier to cite or link to this item:
https://hdl.handle.net/2445/181116
Title: | SSSGAN: Satellite Style and Structure Generative Adversarial Networks |
Author: | Marín Tur, Javier Escalera Guerrero, Sergio |
Keywords: | Imatges satel·litàries Visió per ordinador Aprenentatge automàtic Remote-sensing images Computer vision Machine learning |
Issue Date: | 5-Oct-2021 |
Publisher: | MDPI |
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. |
Note: | Reproducció del document publicat a: https://doi.org/10.3390/rs13193984 |
It is part of: | Remote Sensing, 2021, vol. 13, num. 19 |
URI: | https://hdl.handle.net/2445/181116 |
Related resource: | https://doi.org/10.3390/rs13193984 |
ISSN: | 2072-4292 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
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