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AlgaeNet: A Deep-Learning Framework to Detect Floating Green Algae From Optical and SAR Imagery

文献类型：期刊论文

作者Gao, Le1,2; Li, Xiaofeng1,2; Kong, Fanzhou2,3; Yu, Rencheng2,3; Guo, Yuan1,2; Ren, Yibin1,2
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2022
卷号15页码:2782-2796
ISSN号1939-1404
关键词Algae MODIS Synthetic aperture radar Optical sensors Optical imaging Marine vehicles Spatial resolution Deep learning (DL) green algae detection satellite remote sensing
DOI10.1109/JSTARS.2022.3162387
通讯作者Li, Xiaofeng(lixf@qdio.ac.cn)
英文摘要This article developed a scalable deep-learning model, the AlgaeNet model, for floating Ulva prolifera (U. prolifera) detection in moderate resolution imaging spectroradiometer (MODIS) and synthetic aperture radar (SAR) images. We labeled 1055/4071 pairs of samples, among which 70%/30% were used for training/validation. As a result, the model reached an accuracy of 97.03%/99.83% and a mean intersection over union of 48.57%/88.43% for the MODIS/SAR images. The model was designed based on the classic U-Net model with two tailored modifications. First, the physics information input was a multichannel multisource remote sensing data. Second, a new loss function was developed to resolve the class-unbalanced samples (algae and seawater) and improve model performance. In addition, this model is expandable to process images from optical sensors (e.g., MODIS/GOCI/Landsat) and SAR (e.g., Sentinel-1/GF-3/Radarsat-1 or 2), reducing the potential biases due to the selection of extraction thresholds during the traditional threshold-based segmentation. We process satellite images containing U. prolifera in the Yellow Sea and draw two conclusions. First, adding the 10-m high-resolution SAR imagery shows a 63.66% increase in algae detection based on the 250-m resolution MODIS image alone. Second, we define a floating and submerged ratio number (FS ratio) based on the floating and submerged parts of U. prolifera detected by SAR and MODIS. A research vessel measurement confirms the FS ratio to be a good indicator for representing different life phases of U. prolifera.
资助项目National Natural Science Foundation of China[U2006211] ; National Natural Science Foundation of China[42090044] ; Major Scientific and Technological Innovation Projects in Shandong Province[2019JZZY010102] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42000000] ; Key Project of the Center for Ocean Mega-Science[COMS2019R02] ; Key Project of the Center for Ocean Mega-Science[Y9KY04101L] ; Zhejiang Provincial Natural Science Foundation of China[LR21D060002] ; CAS[Y9KY04101L]
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000784198000004
源URL[http://ir.qdio.ac.cn/handle/337002/178747]  
专题海洋研究所_海洋环流与波动重点实验室
海洋研究所_海洋生态与环境科学重点实验室
通讯作者Li, Xiaofeng
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm Sci, Qingdao 266071, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Le,Li, Xiaofeng,Kong, Fanzhou,et al. AlgaeNet: A Deep-Learning Framework to Detect Floating Green Algae From Optical and SAR Imagery[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2022,15:2782-2796.
APA Gao, Le,Li, Xiaofeng,Kong, Fanzhou,Yu, Rencheng,Guo, Yuan,&Ren, Yibin.(2022).AlgaeNet: A Deep-Learning Framework to Detect Floating Green Algae From Optical and SAR Imagery.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,15,2782-2796.
MLA Gao, Le,et al."AlgaeNet: A Deep-Learning Framework to Detect Floating Green Algae From Optical and SAR Imagery".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15(2022):2782-2796.

入库方式： OAI收割

来源：海洋研究所

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