Multi-label Cloud Segmentation Using a Deep Network

被引：0

作者：

Dev, Soumyabrata ^{[1
]}

Manandhar, Shilpa ^{[2
]}

Lee, Yee Hui ^{[2
]}

Winkler, Stefan ^{[3
]}

机构：

[1] Trinity Coll Dublin, ADAPT SFI Res Ctr, Dublin, Ireland

[2] Nanyang Technol Univ NTU, Sch Elect & Elect Engn, Singapore, Singapore

[3] Natl Univ Singapore NUS, Sch Comp, Singapore, Singapore

来源：

2019 USNC-URSI RADIO SCIENCE MEETING (JOINT WITH AP-S SYMPOSIUM) | 2019年

关键词：

D O I：

10.1109/usnc-ursi.2019.8861850

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Different empirical models have been developed for cloud detection. There is a growing interest in using the ground-based sky/cloud images for this purpose. Several methods exist that perform binary segmentation of clouds. In this paper, we propose to use a deep learning architecture (U-Net) to perform multi-label sky/cloud image segmentation. The proposed approach outperforms recent literature by a large margin.

引用

页码：113 / 114

页数：2

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