Research on cloud parameter inversion method based on deep learning

Wenhan WU; Jinji MA; Erchang SUN; Jinyu GUO; Guang YANG; Yuyao WANG

doi:10.3969/j.issn.1673-6141.2022.04.007

[1] Rossow W B, Mosher F, Kinsella E, et al. ISCCP cloud algorithm intercomparison [J]. Journal of Climate and Applied Meteorology, 1985, 24(9): 877-903.

[2] Yang J, Chen B J, Yin Y. Physics of Clouds and Precipitation [M]. Beijing: Meteorological Press, 2011.

[3] Li W B. Atmospheric Remote Sensing [M]. Beijing: Peking University Press, 2014.

[4] Strabala K I, Ackerman S A, Menzel W P. Cloud properties inferred from 8-12-μm data [J]. Journal of Applied Meteorology, 1994, 33(2): 212-229.

[5] Baum B A, Soulen P F, Strabala K I, et al. Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase [J]. Journal of Geophysical Research: Atmospheres, 2000, 105(D9): 11781-11792.

[6] Segal-Rozenhaimer M, Li A L, Das K, et al. Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN) [J]. Remote Sensing of Environment, 2020, 237: 111446.

[7] He Y M, Wang H J, Guan H. Application of support vector machines in cloud detection using EOS/MODIS [C]. International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 2009, 7383: 209-216.

[8] Yang C P, Guo J. Improved cloud phase retrieval approaches for China's FY-3A/VIRR multi-channel data using artificial neural networks [J]. Optik, 2016, 127(4): 1797-1803.

[9] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507.

[10] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

[11] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90.

[12] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition [C]. IEEE Conference on Computer Vision and Pattern Recognition. June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 770-778.

[13] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation [C]. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, 2015: 234-241.

[14] Altini N, Cascarano G D, Brunetti A, et al. Semantic segmentation framework for glomeruli detection and classification in kidney histological sections [J]. Electronics, 2020, 9(3): 503.

[15] Hu X L, Liu Z Y, Zhou H Y, et al. Deep HT: A deep neural network for diagnose on MR images of tumors of the hand [J]. PLoS One, 2020, 15(8): e0237606.

[16] Huang X D, Zhang H, Zhuo L, et al. TISNet-enhanced fully convolutional network with encoder-decoder structure for tongue image segmentation in traditional Chinese medicine [J]. Computational and Mathematical Methods in Medicine, 2020, 2020: 6029258.

[17] Guo H X, He G J, Jiang W, et al. A multi-scale water extraction convolutional neural network (MWEN) method for GaoFen-1 remote sensing images [J]. ISPRS International Journal of Geo-Information, 2020, 9(4): 189.

[18] Wang G J, Wu M J, Wei X K, et al. Water identification from high-resolution remote sensing images based on multidimensional densely connected convolutional neural networks [J]. Remote Sensing, 2020, 12(5): 795.

[19] Liu P H, Liu X P, Liu M X, et al. Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network [J]. Remote Sensing, 2019, 11(7): 830.

[20] Shi Q, Liu M X, Liu X P, et al. Domain adaption for fine-grained urban village extraction from satellite images [J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(8): 1430-1434.

[21] Wang Y H, Liang B X, Ding M, et al. Dense semantic labeling with atrous spatial pyramid pooling and decoder for high-resolution remote sensing imagery [J]. Remote Sensing, 2018, 11(1): 20.

[22] Yao X D, Yang H, Wu Y L, et al. Land use classification of the deep convolutional neural network method reducing the loss of spatial features [J]. Sensors (Basel), 2019, 19(12): 2792.

[23] Jiang N, Li J Y. An improved semantic segmentation method for remote sensing images based on neural network [J]. Traitement Du Signal, 2020, 37(2): 271-278.

[24] Zhan Z Q, Zhang X M, Liu Y, et al. Vegetation land use/land cover extraction from high-resolution satellite images based on adaptive context inference [J]. IEEE Access, 2020, 8: 21036-21051.

[25] Venugopal N. Automatic semantic segmentation with DeepLab dilated learning network for change detection in remote sensing images [J]. Neural Processing Letters, 2020, 51(3): 2355-2377.

[26] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation [C]. Computer Vision-ECCV 2018, 2018: 833-851.

[27] He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.

[28] Ben-Dor E. A precaution regarding cirrus cloud detection from airborne imaging spectrometer data using the 1.38 μm water vapor band [J]. Remote Sensing of Environment, 1994, 50(3): 346-350.

[29] Wang L Y. Research on Semantic Segmentation of Remote Sensing Images of the Ground Objects Based on Deeplab V3+ Network [D]. Harbin: Harbin Institute of Technology, 2020.