Journal of Infrared and Millimeter Waves, Volume. 41, Issue 5, 894(2022)

Prediction of independent sea ice motion in Arctic channel based on Multiloss-SAM-ConvLSTM

Fu-Qiang ZHENG1,2, Ding-Bo KUANG2, Yong HU2、*, Cai-Lan GONG2, and Shuo HUANG2
Author Affiliations
  • 1University of Chinese Academy of Sciences,Beijing 100049,China
  • 2Key Laboratory of Infrared System Detection and Imaging Technology,Shanghai Institute of Technical Physics Chinese Academic of Sciences,Shanghai 200083,China
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    References(22)

    [1] WU Zhan-Kai, WANG Xing-Dong, WANG Cheng. Sea Ice Distribution Detection Based on FY-3 Satellite MWRI Data[J]. Bulletin of Surveying and Mapping, 0, 56-60, 65(2018).

    [2] Petrou Z I, Tian Y. Prediction of sea ice motion with convolutional long short-term memory networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 6865-6876(2019).

    [3] Ayzel Georgy, Heistermann Maik, Winterrath Tanja. Optical flow models as an open benchmark for radar-based precipitation nowcasting[J]. Geoscientific Model Development, 12(2019).

    [4] Cheung P, Yeung H Y. Application of optical-flow technique to significant convection nowcast for terminal areas in Hong Kong[C], 6-10(2012).

    [5] Sakaino Hidetomo. Spatio-Temporal Image Pattern Prediction Method Based on a Physical Model With Time-Varying Optical Flow[J]. IEEE Transactions on Geoscience and Remote Sensing, 51(2013).

    [6] Liu Y, Xi D G, Li Z L et al. A new methodology for pixel-quantitative precipitation nowcasting using a pyramid Lucas Kanade optical flow approach[J]. Journal of Hydrology, 529, 354-364(2015).

    [7] SHI Yu-li, SHI Sheng-wei. Research on Accuracy Evaluation of Optical Flow Algorithm in FY4A Infrared Image Extrapolation[J]. Journal of Ordnance Equipment Engineering, 42, 150-158,224(2021).

    [8] SHI Xingjian, Chen Z, Wang H et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting[C], 802-810(2015).

    [9] Ranzato Marc'Aurelio, Szlam Arthur, Bruna Joan et al. Video (language) modeling: a baseline for generative models of natural videos[J](2014).

    [10] Srivastava N, Mansimov E, Salakhudinov R. Unsupervised Learning of Video Representations using LSTMs[C], 843-852(2015).

    [11] Wang Y, Long M, Wang J et al. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms[C], 879-888(2017).

    [12] Wang Y, Gao Z, Long M et al. Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning[C], 5123-5132(2018).

    [13] Wang Y, Zhang J, Zhu H et al. Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics[C], 9154-9162(2019).

    [14] He K, Zhang X, Ren S et al. Deep residual learning for image recognition[C], 770-778(2016).

    [15] Shi X, Gao Z, Lausen L et al. Deep learning for precipitation nowcasting: A benchmark and a new model[J](2017).

    [16] Lin Z, Li M, Zheng Z et al. Self-attention convlstm for spatiotemporal prediction[C], 34, 11531-11538(2020).

    [17] Kang D, Im J, Lee M I et al. The MODIS ice surface temperature product as an indicator of sea ice minimum over the Arctic Ocean[J]. Remote sensing of environment, 152, 99-108(2014).

    [18] ZHENG Fu-Qiang, KUANG Ding-Bo, HU Yong et al. Refined segmentation method based on U-ASPP-Net for Arctic independent sea ice[J]. J.Infrared Millim.Waves, 40, 798-808(2021).

    [19] ZHOU Ying. The Research on Monitoring the Motion and Change of the Arctic Sea IceBased on the FY-3 Meteorological Satellite Data[D](2013).

    [20] ZHOU Ying, GONG Cai-Lan, HU Yong et al. Extraction of Sea Ice Information in the Arctic Ocean by Using FY-3/MERSI Data[J]. Journal of Atmospheric and Environmental Optics, 8, 53-59(2013).

    [21] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 5998-6008(2017).

    [22] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C], 2980-2988(2017).

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    Fu-Qiang ZHENG, Ding-Bo KUANG, Yong HU, Cai-Lan GONG, Shuo HUANG. Prediction of independent sea ice motion in Arctic channel based on Multiloss-SAM-ConvLSTM[J]. Journal of Infrared and Millimeter Waves, 2022, 41(5): 894

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    Paper Information

    Category: Research Articles

    Received: Jan. 28, 2022

    Accepted: --

    Published Online: Feb. 6, 2023

    The Author Email: Yong HU (huyong@mail.sitp.ac.cn)

    DOI:10.11972/j.issn.1001-9014.2022.05.014

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