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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:
It is part of: Remote Sensing, 2021, vol. 13, num. 19
Related resource:
ISSN: 2072-4292
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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