[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] Kwok R. Arctic sea ice thickness, volume, and multiyear ice coverage: Losses and coupled variability (1958-2018)[J]. Environmental Research Letters, 13, 105005(2018).

[3] Abe M, Nozawa T, Ogura T et al. Effect of retreating sea ice on Arctic cloud cover in simulated recent global warming[J]. Atmospheric Chemistry and Physics, 16, 1-43(2016).

[4] Gareth Rees W[M]. Snow and ice remote sensing(2011).

[5] LIU Liang-Ming[M]. An introduction to satellite ocean remote sensing(2005).

[6] 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).

[7] Han X Z, Yang J, Tang S H et al. Vegetation products derived from Fengyun-3D medium resolution spectral imager-Ⅱ[J]. Journal of Meteorological Research, 34, 119-129(2020).

[8] ZHOU Ying, KUANG Ding-Bo, GONG Cai-Lan et al. A method to extract parameters of Arctic sea ice from FY-3/MERSI imagery[J]. J.Infrared Millim.Waves, 36, 41-49(2017).

[9] Wang W, Wen Y, Xue D et al. Sea ice classification of SAR image based on wavelet transform and gray level co-occurrence matrix[C], 29, 104-107(2015).

[10] Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man and Cybernetics, SMC-3, 610-621(1973).

[11] Liu H, Guo H, Zhang L. SVM-based sea ice classification using textural features and concentration from RADARSAT-2 dual-pol ScanSAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 8, 1601-1613(2015).

[12] SHENG Yang, LANG Wen-Hui, WU Jie et al. Combining MRF and v-SVM for SAR sea ice image classification[J]. Journal of Remote Sensing, 19, 844-855(2015).

[13] HAN Yan-Ling, ZHAO Yao, ZHOU Ru-Yan et al. Cooperative active learning and semi-supervised method for sea ice image classification[J]. Haiyang Xuebao, 42, 123-135(2020).

[14] ZHAO Chao-Fang, XU Rui, ZHAO Ke. Research of polar sea ice detection methods based on JY-2A/SCAT[J]. Periodical of Ocean University of China, 49, 140-149(2019).

[15] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2015).

[16] Chen L C, Papandreou G, Kokkinos I et al. Semantic image segmentation with deep convolutional nets and fully connected crfs[J]. arXiv preprint arXiv, 14127062(2014).

[17] Chen L C, Papandreou G, Kokkinos I et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE transactions on pattern analysis and machine intelligence, 40, 834-848(2017).

[18] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv:170605587(2017).

[19] Chen L C, Zhu Y, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C], 801-818(2018).

[20] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C], 234-241(2015).

[21] 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).

[22] Chollet F. Xception: Deep learning with depthwise separable convolutions[C], 1251-1258(2017).

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

[24] Winther J-G. Spectral bi-directional reflectance of snow and glacier ice measured in Dronning Maud Land, Antarctica[J]. Annals of Glaciology, 20, 1-5(1994).

[25] WU Bo, LIN Shan-Shan, ZHOU Gui-Jun. Quantitatively Evaluating Indexes for Object-based Segmentation of High Spatial Resolution Image[J]. Journal of Geo-information Science, 15, 567-573(2013).