[1] Fabel Y, Nouri B, Wilbert S et al. Applying self-supervised learning for semantic cloud segmentation of all-sky images[J]. Atmospheric Measurement Techniques, 15, 797-809(2022).

[2] Shaffery P, Habte A, Netto M et al. Automated construction of clear-sky dictionary from all-sky imager data[J]. Solar Energy, 212, 73-83(2020).

[3] Azhar M A D M, Hamid N S A, Kamil W M A W M et al. Daytime cloud detection method using the all-sky imager over PERMATApintar observatory[J]. Universe, 7, 41(2021).

[4] Sun X J, Liu L, Gao T C et al. Cloud classification of the whole sky infrared image based on the fuzzy uncertainty texture spectrum[J]. Journal of Applied Meteorological Science, 20, 157-163(2009).

[5] Cazorla A, Olmo F J, Alados-Arboledas L. Development of a sky imager for cloud cover assessment[J]. Journal of the Optical Society of America A, 25, 29(2007).

[6] Liu L, Sun X J, Chen F et al. Cloud classification based on structure features of infrared images[J]. Journal of Atmospheric and Oceanic Technology, 28, 410-417(2011).

[7] Kaur R, Ganju A. Cloud classification in NOAA AVHRR imageries using spectral and textural features[J]. Journal of the Indian Society of Remote Sensing, 36, 167-174(2008).

[8] Li J, Menzel W P, Yang Z D et al. High-spatial-resolution surface and cloud-type classification from MODIS multispectral band measurements[J]. Journal of Applied Meteorology, 42, 204-226(2003).

[9] Zhu Y P, Bai J, Liu J W et al. Multi-spectral cloud classification based on EOS/MODI image data[M]. Selected Papers of the 14th National Academic Exchange Conference on Remote Sensing Technology, 6(2003).

[10] Zhu Y P, Cheng Z J, Liu J W et al. Application of multi-spectral cloud classification technique in frontal cloud system[J]. Journal of Tropical Meteorology, 25, 66-72(2009).

[11] Jin W, Gong F, Tang B et al. Cloud types identification for meteorological satellite image using multiple sparse representation classifiers via decision fusion[J]. IEEE Access, 7, 8675-8688(2019).

[12] Shenk W E, Holub R J, Neff R A. A multispectral cloud type identification method developed for tropical ocean areas with nimbus-3 MRIR measurements[J]. Monthly Weather Review, 104, 284-291(1976).

[13] Inoue T. A cloud type classification with NOAA 7 split-window measurements[J]. Journal of Geophysical Research: Atmospheres, 92, 3991-4000(1987).

[14] Zhu B, Yang J, Lü W T et al. Ground-based visible cloud image classification method based on KNN algorithm[J]. Journal of Applied Meteorogical Science, 23, 721-728(2012).

[15] Heinle A, Macke A, Srivastav A. Automatic cloud classification of whole sky images[J]. Atmospheric Measurement Techniques, 3, 557-567(2010).

[16] Zhuo W, Cao Z G, Xiao Y. Cloud classification of ground-based images using texture-structure features[J]. Journal of Atmospheric and Oceanic Technology, 31, 79-92(2014).

[17] Ye L, Cao Z G, Xiao Y et al. Supervised fine-grained cloud detection and recognition in whole-sky images[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 7972-7985(2019).

[18] Tang Y Z, Yang P L, Zhou Z M et al. Improving cloud type classification of ground-based images using region covariance descriptors[J]. Atmospheric Measurement Techniques, 14, 737-747(2021).

[19] Wang Y, Wang C H, Shi C Z et al. A selection criterion for the optimal resolution of ground-based remote sensing cloud images for cloud classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 1358-1367(2019).

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

[21] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[22] Ma S J, Hao S Q, Zhao Q S et al. Atmospheric turbulence intensity estimation based on deep convolutional neural networks[J]. Chinese Journal of Lasers, 48, 0401018(2021).

[23] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).

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

[25] Deng Z L, Li L. Chinese food recognition model based on improved residual network[J]. Laser & Optoelectronics Progress, 58, 0610019(2021).

[26] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[27] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[28] Yang Q W, Zhou K. Press-plate state recognition based on improved bilinear fine-grained model[J]. Laser & Optoelectronics Progress, 58, 2010007(2021).

[29] Shi M Y, Xie F Y, Zi Y et al. Cloud detection of remote sensing images by deep learning[C], 701-704(2016).

[30] Ye L, Cao Z G, Xiao Y et al. Ground-based cloud image categorization using deep convolutional visual features[C], 4808-4812(2015).

[31] Ma J Y, Zhang T J, Jing G D et al. Notice of violation of IEEE publication principles: ground-based cloud image recognition system based on multi-CNN and feature screening and fusion[J]. IEEE Access, 8, 173949-173960(2020).

[32] Ye L, Cao Z G, Xiao Y. DeepCloud: ground-based cloud image categorization using deep convolutional features[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 5729-5740(2017).

[33] Shi C Z, Wang C H, Wang Y et al. Deep convolutional activations-based features for ground-based cloud classification[J]. IEEE Geoscience and Remote Sensing Letters, 14, 816-820(2017).

[34] Phung V H, Rhee E J. A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets[J]. Applied Sciences, 9, 4500(2019).

[35] Zhang J L, Liu P, Zhang F et al. CloudNet: ground-based cloud classification with deep convolutional neural network[J]. Geophysical Research Letters, 45, 8665-8672(2018).

[36] Liu S, Li M, Zhang Z et al. Multi-evidence and multi-modal fusion network for ground-based cloud recognition[J]. Remote Sensing, 12, 464(2020).

[37] Zhao M Y, Chang C H, Xie W B et al. Cloud shape classification system based on multi-channel CNN and improved FDM[J]. IEEE Access, 8, 44111-44124(2020).