| 国产高分辨率卫星影像云检测方法分析 |
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| 引用本文： | 刘云峰,杨珍,韩骁,付俊.国产高分辨率卫星影像云检测方法分析[J].测绘通报,2020,0(11):66-70. |
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| 作者姓名： | 刘云峰 杨珍 韩骁 付俊 |
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| 作者单位： | 1. 自然资源部第一航测遥感院, 陕西 西安 710054;2. 西安中科星图空间数据技术有限公司, 陕西 西安 710199 |
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| 摘 要： | 云检测方法大都针对特定的传感器或依赖多个波段，对参数要求高，而国产高分辨率卫星影像通常包含波段数较少，多数云检测方法不适用。本文采用深度学习的方法，以融合后的高分一号影像为例，应用基于双重视觉注意机制模型进行云检测，并与人工采集、全卷积网络模型的检测结果进行对比。理论分析和研究结果表明：基于双重视觉注意机制的模型云检测结果与人工采集进行对比，正确率为0.986 4；通过增加云样本数量和非云样本数量可有效解决模型对道路、河流、居民地的误检测问题；基于双重视觉注意机制的模型与全卷积网络模型相比，云边界更为准确，模型适用性更强。利用较少的波段信息进行云检测为国产其他高分辨率卫星影像云检测提供了参考。
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| 关 键 词： | 高分辨率 样本 云检测 全卷积网络 视觉注意 |
| 收稿时间： | 2020-06-10 |
| 修稿时间： | 2020-07-15 |
Research on cloud detection method of domestic high-resolution satellite images |
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| LIU Yunfeng,YANG Zhen,HAN Xiao,FU Jun.Research on cloud detection method of domestic high-resolution satellite images[J].Bulletin of Surveying and Mapping,2020,0(11):66-70. |
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| Authors: | LIU Yunfeng YANG Zhen HAN Xiao FU Jun |
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| Institution: | 1. The First Institute of Photogrammetry and Remote Sensing, MNR, Xi'an 710054, China;2. Xi'an Geovis Spatial Data Technology Co., Ltd., Xi'an 710199, China |
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| Abstract: | Most cloud detection methods aim at specific sensors or rely on multiple bands, which require high parameters. However, domestic high-resolution satellite images usually contain a small number of bands, and most cloud detection methods are not applicable. In this paper, the deep learning method is adopted, the fused GF-1 images are innovatively applied to the “dualattention mechanism” model for cloud detection,and compared with the test results of manual drawing and full convolutional network model. Theoretical analysis and research results show that: first, the cloud detection efficiency results of the “dual attention mechanism” model are compared with the results of manual drawing, the accuracy rate is 0.986 4. Second, by increasing the number of cloud samples and the number of non-cloud samples, the model can effectively solve the problem of misdetection of roads, rivers, residential areas. Third, compared with the full convolutional network model, the dual attention mechanism model has more accurate cloud boundaries and stronger model applicability. Using less band information for cloud detection provides a reference for other domestic high-resolution satellite images cloud detection. |
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| Keywords: | high-resolution sample cloud detection fully convolutional network visual attention |
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