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Article

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Published version

Publication date

2021-10-05

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cc-by (c) Marín, Javier et al., 2021
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/181116

SSSGAN: Satellite Style and Structure Generative Adversarial Networks

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https://doi.org/10.3390/rs13193984

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.

Subject

Imatges satel·litàries, Visió per ordinador, Aprenentatge automàtic

Subject (English)

Remote-sensing images, Computer vision, Machine learning

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Articles publicats en revistes (Matemàtiques i Informàtica)

Citation

MARÍN TUR, Javier and ESCALERA GUERRERO, Sergio. SSSGAN: Satellite Style and Structure Generative Adversarial Networks. Remote Sensing. 2021. Vol. 13, num. 19. ISSN 2072-4292. [consulted: 12 of August of 2026]. Available at: https://hdl.handle.net/2445/181116

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