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发表刊物：2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
刊物所在地：Brussels, Belgium
摘要：The dependence on large-scale pixel-level annotations brings great challenge to semantic segmentation task for remote sensing images (RSIs). To alleviate this issue, we propose V2RNet, an unsupervised semantic segmentation method which introduces adversarial learning into segmentation network. Our method creatively transfers the segmentation model from the synthetic GTA-V data to the real optical remote sensing data via domain adaptation. Additionally, to unify the source domain semantic structures and target domain image style, we design a semantic segmentation discriminator as auxiliary to optimize the domain adaptation efficiency. Thus the proposed method is effective on typical remote sensing targets such densely arranged, intertwined road. Experimental results on Massachusetts Road data set demonstrate our unsupervised semantic segmentation model achieves comparable segmentation accuracy, which also validates the effectiveness of the proposed method.
第一作者：赵丹培
论文类型：Conference paper
通讯作者：赵丹培
一级学科：控制科学与工程
页面范围：4676-4679
ISSN号：2153-7003
是否译文：否
发表时间：2021-07-11
收录刊物：EI
发布期刊链接：https://ieeexplore.ieee.org/document/9553290