双月刊

ISSN 1006-9895

CN 11-1768/O4

基于多尺度特征融合网络的云和云阴影检测试验
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1.中国气象局中国遥感卫星辐射测量和定标重点开放实验室 国家卫星气象中心;2.南京信息工程大学江苏省大气环境与装备技术协同创新中心;3.中国科学院空间应用工程与技术中心

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Cloud and Cloud Shadow Detection Test Based on Multi-scale Feature Fusion Network
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Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology

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    摘要:

    目前,基于深度学习的高分辨率光学影像云检测过程中,云和云阴影及其边缘细节丢失较为严重,主要原因在于不同尺度空间语义信息特征融合存在不足。针对该问题,本文构建一种基于深度学习的多尺度特征融合网络(Multi-scale Feature Fusion Network, MFFN)的云和云阴影检测方法,该算法结合防止网络退化的残差神经网络模块(Res.block)、扩大网络感受野的多尺度卷积模块(MCM)和提取并融合不同尺度信息的多尺度特征模块(MFM)。实验表明,本算法能提取丰富的空间信息与语义信息,可取得较为精细的云与云阴影掩摸,具有较高检测精度,其中云检测准确率达0. 9796,云阴影检测准确率达0.8307。同时,该工作可为深度学习技术应用于业务云检测提供理论支持及技术储备。

    Abstract:

    At present, the cloud detection based on high-resolution optical images, with deep learning methodology, cannot provide adequate and accurate information about the cloud, the cloud shadows or their edge details. The main reason lies in the insufficient fusion of semantic information in different scales of classification techniques. In response to this problem, this paper combines Res.block module that can prevent network degradation, the multi-scale convolution module (MCM) that can increase the receptive field of the network and the multi-scale feature module (MFM) that can extract and integrate information from different scales and proposes a detection algorithm based on Multi-scale Feature Fusion Network (MFFN) based on deep learning. The experimental results show that rich spatial information as well as semantic information can be extracted by the algorithm. Cloud and cloud shadow masks with higher level of accuracy can also be acquired. The accuracy of the cloud detection is 0.9351, and the accuracy of the cloud shadow detection is 0.8103. Meanwhile, the study provides theoretical support and technical reserve for the application of deep learning techniques on operational cloud detection.

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历史
  • 收稿日期:2020-10-16
  • 最后修改日期:2021-05-11
  • 录用日期:2021-05-28
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