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博士生李志伟的论文在ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 刊出
发布时间:2019-04-29 10:49:23     发布者:易真     浏览次数:

标题: Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors

作者: Li, ZW (Li, Zhiwei); Shen, HF (Shen, Huanfeng); Cheng, Q (Cheng, Qing); Liu, YH (Liu, Yuhao); You, SC (You, Shucheng); He, ZY (He, Zongyi)

来源出版物: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING  : 150  : 197-212  DOI: 10.1016/j.isprsjprs.2019.02.017  出版年: APR 2019  

摘要: Cloud detection is an important preprocessing step for the precise application of optical satellite imagery. In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors. In the network architecture of MSCFF, the symmetric encoder-decoder module, which provides both local and global context by densifying feature maps with trainable convolutional filter banks, is utilized to extract multi-scale and high-level spatial features. The feature maps of multiple scales are then up-sampled and concatenated, and a novel multi-scale feature fusion module is designed to fuse the features of different scales for the output. The two output feature maps of the network are cloud and cloud shadow maps, which are in turn fed to binary classifiers outside the model to obtain the final cloud and cloud shadow mask. The MSCFF method was validated on hundreds of globally distributed optical satellite images, with spatial resolutions ranging from 0.5 to 50 m, including Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and collected high-resolution images exported from Google Earth. The experimental results show that MSCFF achieves a higher accuracy than the traditional rule-based cloud detection methods and the state-of-the-art deep learning models, especially in bright surface covered areas. The effectiveness of MSCFF means that it has great promise for the practical application of cloud detection for multiple types of medium and high-resolution remote sensing images. Our established global high-resolution cloud detection validation dataset has been made available online (http://sendimage.whu.edu.cn/en/mscff/).

入藏号: WOS:000464088400014

语言: English

文献类型: Article

作者关键词: Cloud detection; Cloud shadow; Convolutional neural network; Multi-scale; Convolutional feature fusion; MSCFF

地址: [Li, Zhiwei; Shen, Huanfeng; Liu, Yuhao; He, Zongyi] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China.

[Shen, Huanfeng] Collaborat Innovat Ctr Geospatial Technol, Wuhan, Hubei, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, Wuhan, Hubei, Peoples R China.

[Cheng, Qing] Wuhan Univ, Sch Urban Design, Wuhan, Hubei, Peoples R China.

[You, Shucheng] China Land Surveying & Planning Inst, Dept Remote Sensing, Beijing, Peoples R China.

通讯作者地址: Shen, HF (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China.

Cheng, Q (通讯作者)Wuhan Univ, Sch Urban Design, Wuhan, Hubei, Peoples R China.

电子邮件地址: shenhf@whu.edu.cn; qingcheng@whu.edu.cn

影响因子:5.994


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