TY - JOUR AU - Javier Marin AU - Sergio Escalera PY - 2021// TI - SSSGAN: Satellite Style and Structure Generative Adversarial Networks JO - Remote Sensing SP - 3984 VL - 13 IS - 19 N2 - 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 produceconsistent 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. UR - https://doi.org/10.3390/rs13193984 L1 - http://refbase.cvc.uab.es/files/MaE2021.pdf N1 - HUPBA; no proj ID - Javier Marin2021 ER -