Spectroscopy and Spectral Analysis, Volume. 43, Issue 2, 608(2023)
An Inversion Method for Chlorophyll-a Concentration in Global Ocean Through Convolutional Neural Networks
[2] [2] Emanuele Organelli, Annick Bricaud, Bernard Gentili, et al. Remote Sensing of Environment, 2016, 186: 297.
[3] [3] Nguyen Ha, Katsuaki Koike, Mai Nhuan. Remote Sensing, 2013, 6(1): 421.
[4] [4] Smith M E, Lain L Robertson, Bernard S. Remote Sensing of Environment, 2018, 215: 217.
[5] [5] Miao S, Lyu H, Wang Q, et al. Ecological Indicators, 2019, 101: 399.
[6] [6] Li X, Sha J, Wang Z L. Environmental Science and Pollution Research, 2018, 25(20): 19488.
[7] [7] Kong Xianyu, Che Xiaowei, Su Rongguo, et al. Journal of Oceanology and Limnology, 2017, 36(2): 249.
[10] [10] Yu B W, Xu L L, Peng J H, et al. Journal of Applied Remote Sensing, 2020, 14(3): 034520.
[11] [11] Gu J X, Wang Z H, Kuen J, et al. Pattern Recognition, 2018, 77(5): 354.
[12] [12] Makantasis K, Karantzalos K, Doulamis A, et al. Deep Supervised Learning for Hyperspectral Data Classification Through Convolutional Neural Networks. IEEE International Geoscience and Remote Sensing Symposium, 2015. 4959.
[16] [16] Igor Sevo, Aleksej Avramovic. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 740.
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SUN Xi-tong, FU Yun, HAN Chun-xiao, FAN Yu-hua, WANG Tian-shu. An Inversion Method for Chlorophyll-a Concentration in Global Ocean Through Convolutional Neural Networks[J]. Spectroscopy and Spectral Analysis, 2023, 43(2): 608
Received: Sep. 7, 2021
Accepted: --
Published Online: Mar. 28, 2023
The Author Email: Xi-tong SUN (xitong943@foxmail.com)