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Multi-Discriminator Generative Adversarial Network for High Resolution Gray-Scale Satellite Image Colorization
Li FM(李非墨); Ma L(马雷); Cai J(蔡健)
2018-11
Conference NameIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Conference Date22-27 July 2018
Conference PlaceValencia, Spain
Abstract

Automatic colorization for grayscale satellite images can help with eliminating lighting differences between multi-spectral captures, and provides strong prior information for ground type classification and object detection. In this paper, we introduced a novel generative adversarial network with multiple discriminators for colorizing gray-scale satellite images with pseudo-natural appearances. Although being powerful, deep generative model in its common form with a single discriminator could be unstable for achieving spatial consistency on local textured regions, especially highly textured ones. To address this issue, the generator in our proposed structure produces a group of colored outputs from feature maps at different scale levels of the network, each being supervised by an independent discriminator to fit the original colored training input in discrete Lab color space. The final colored output is a cascaded ensemble of these preceding by-products via summation, thus the fitting errors are reduced by a geometric series form. Quantitative and qualitative comparisons with the sole-discriminator version have been performed on highresolution satellite images in experiments, where significant reductions in prediction errors have been observed.

Keywordpseudo-natural colorization gray-scale satellite images generative adversarial network multiple discriminators
Subject Area模式识别
MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
DOI10.1109/IGARSS.2018.8517930
URL查看原文
Indexed ByEI
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26077
Collection综合信息系统研究中心
Corresponding AuthorLi FM(李非墨); Ma L(马雷)
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li FM,Ma L,Cai J. Multi-Discriminator Generative Adversarial Network for High Resolution Gray-Scale Satellite Image Colorization[C],2018.
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