Efficient cloud detection in remote sensing images using edge-aware segmentation network and easy-to-hard training strategy | |
Yuan, Kun; Meng, Gaofeng; Cheng, Dongcai; Bai, Jun; Xiang, Shiming; Pan, Chunhong | |
2017 | |
会议日期 | September 17-20, 2017 |
会议地点 | Beijing, China |
页码 | 61-65 |
英文摘要 |
Detecting cloud regions in remote sensing image (RSI) is very challenging yet of great importance to meteorological forecasting and other RSI-related applications. Technically, this task is typically implemented as a pixel-level segmentation. However, traditional
methods based on handcrafted or low-level cloud features often fail to achieve satisfactory performances from images with bright noncloud and/or semitransparent cloud regions. What is more, the performances could be further degraded due to the ambiguous boundaries caused by complicated textures and non-uniform distribution of intensities. In this paper, we propose a multi-task based deep neural network for cloud detection in RSIs. Architecturally, our network is designed to combine the two tasks of cloud segmentation
and cloud edge detection together to encourage a better detection near cloud boundaries, resulting in an end-to-end approach for accurate cloud detection. Accordingly, an efficient sample selection strategy is proposed to train our network in an easy-to-hard manner,
in which the number of the selected samples is governed by a weight that is annealed until the entire training samples have been considered. Both visual and quantitative comparisons are conducted on RSIs collected from Google Earth. The experimental results
indicate that our method can yield superior performance over the state-of-the-art methods. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/15517] |
专题 | 自动化研究所_空天信息研究中心 |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yuan, Kun,Meng, Gaofeng,Cheng, Dongcai,et al. Efficient cloud detection in remote sensing images using edge-aware segmentation network and easy-to-hard training strategy[C]. 见:. Beijing, China. September 17-20, 2017. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论