A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection
文献类型：期刊论文
| 作者 | Ma, Nan1; Sun, Lin2; Zhou, Chenghu3; He, Yawen4; Dong, Chuanxiang2; Qu, Yu2; Yu, Huiyong2 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2023 |
| 卷号 | 16页码:3294-3308 |
| 关键词 | Cloud detection difference-based samples land surface reflectance (LSR) dataset moderate resolution imaging spectrometer (MODIS) multiscale feature |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2023.3261326 |
| 通讯作者 | Sun, Lin(sunlin6@126.com) |
| 英文摘要 | Moderate resolution imaging spectrometer (MODIS) images are widely used in land, ocean, and atmospheric monitoring, due to their wide spectral coverage, high temporal resolution, and convenient data acquisition. Accurate cloud detection is critical to the fine processing and application of MODIS images. Owing to spatial resolution limitations and the influence of mixed pixels, most MODIS cloud detection algorithms struggle to effectively recognize of clouds and ground objects. Here, we propose a novel cloud detection method based on land surface reflectance and a multiscale feature convolutional neural network to achieve high-precision cloud detection, particularly for thin clouds and clouds over bright surface. A monthly surface reflectance dataset was constructed by MODIS products (MOD09A1) and employed to provide background information for cloud detection. Difference-based samples were obtained using surface reflectance as well MODIS images of different phases based on difference operations. The multiscale feature network (MFCD-Net) using an atrous spatial pyramid pooling and a channel and spatial attention module integrated low-level spatial features and high-level semantic information to capture multiscale features and generate a high-precision cloud mask. For cloud detection experiments and quantitative analysis, 61 MODIS images acquired at different times on various underlying surface types were used. Cloud detection results were compared to those of UNet, Deeplabv3+, UNet++, PSPNet, and top of atmosphere-based (MFCD-TOA) methods. The proposed method performed well, with the highest overall accuracy (96.55%), precision (92.13%), and recall (88.90%). It improved cloud detection accuracy in various scenarios, reducing thin cloud omission and bright surface misidentification. |
| WOS关键词 | REMOTE-SENSING IMAGES ; DETECTION ALGORITHM ; SHADOW DETECTION ; CLEAR-SKY |
| 资助项目 | National Natural Science Foundation of China[42271412] ; National Natural Science Foundation of China[41976184] ; Natural Science Foundation of Shandong Province[ZR2020MD051] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000970727300002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/196974]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Sun, Lin |
| 作者单位 | 1.China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China 2.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.China Univ Petr, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Nan,Sun, Lin,Zhou, Chenghu,et al. A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2023,16:3294-3308. |
| APA | Ma, Nan.,Sun, Lin.,Zhou, Chenghu.,He, Yawen.,Dong, Chuanxiang.,...&Yu, Huiyong.(2023).A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,16,3294-3308. |
| MLA | Ma, Nan,et al."A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16(2023):3294-3308. |
入库方式： OAI收割
来源：地理科学与资源研究所
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