Yan, D.;                     Li, G.;                     Li, X.;                     Zhang, H.;                     Lei, H.;                     Lu, K.;                     Cheng, M.;                     Zhu, F.    
        An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images. Remote Sens. 2021, 13, 2052.
    https://doi.org/10.3390/rs13112052
    AMA Style
    
                                Yan D,                                 Li G,                                 Li X,                                 Zhang H,                                 Lei H,                                 Lu K,                                 Cheng M,                                 Zhu F.        
                An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images. Remote Sensing. 2021; 13(11):2052.
        https://doi.org/10.3390/rs13112052
    
    Chicago/Turabian Style
    
                                Yan, Dongchuan,                                 Guoqing Li,                                 Xiangqiang Li,                                 Hao Zhang,                                 Hua Lei,                                 Kaixuan Lu,                                 Minghua Cheng,                                 and Fuxiao Zhu.        
                2021. "An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images" Remote Sensing 13, no. 11: 2052.
        https://doi.org/10.3390/rs13112052
    
    APA Style
    
                                Yan, D.,                                 Li, G.,                                 Li, X.,                                 Zhang, H.,                                 Lei, H.,                                 Lu, K.,                                 Cheng, M.,                                 & Zhu, F.        
        
        (2021). An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images. Remote Sensing, 13(11), 2052.
        https://doi.org/10.3390/rs13112052