• Journal of Atmospheric and Environmental Optics
  • Vol. 20, Issue 1, 1 (2025)
FU Yashuai1,2,3,*, ZHANG Wenhao1,2,3, JIN Yongtao1,2,3, LIU Qiyue1,2,3..., ZHANG Lili4,5, BING Fangfei1,2,3 and MA Yu1,2,3|Show fewer author(s)
Author Affiliations
  • 1School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering,Langfang 065000, China
  • 2Institute of Remote Sensing Applications, North China Institute of Aerospace Engineering, Langfang 065000, China
  • 3Heibei Spacer Remote Sensing Information Processing and Application of Collaborative Innovation Center,Langfang 065000, China
  • 4National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute,Chinese Academy of Sciences, Beijing 100094, China
  • 5Zhongke Langfang Institute of Spatial Information Applications, Langfang 065001, China
  • show less
    DOI: 10.3969/j.issn.1673-6141.2025.01.001 Cite this Article
    Yashuai FU, Wenhao ZHANG, Yongtao JIN, Qiyue LIU, Lili ZHANG, Fangfei BING, Yu MA. Research progress of cloud classification based on optical satellite remote sensing[J]. Journal of Atmospheric and Environmental Optics, 2025, 20(1): 1 Copy Citation Text show less
    References

    [1] Z H Zhuang, M Wang, K Wang et al. Research progress of of deep learning-based cloud classification. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 14, 566-578(2022).

    [2] Y C Zhang, W B Rossow, A A Lacis et al. Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. Journal of Geophysical Research: Atmospheres, 109, D19105(2004).

    [3] J Y Gong, S P Ji. Photogrammetry and deep learning. Journal of Surveying and mapping, 47, 693-704(2018).

    [4] L Ma. Improving of Rain Area Delineation Schemes Based on Machine Learning Algorithm(2018).

    [5] Y M Huang. The Cloud Physical Parameters Are Retrieved by Using Datellite Data and Their Application in Weather Modification(2006).

    [6] J G Wang, R Zhang, M Hong et al. Synthetical optimization clustering method for classifying cloud from satellite images. Journal of PLA University of Science and Technology (Natural Science Edition), 6, 585-590(2005).

    [7] J Jia, L Q Shao. The research of automatic classification of meteorological satellite imagery in remote sensing field. Wireless Internet Technology, 120-123(2016).

    [8] J Song, S H Gao, Y Q Zhu et al. A survey of remote sensing image classification based on CNNs. Big Earth Data, 3, 232-254(2019).

    [9] G Li, X H Kong. Overview and development trends of high-resolution optical imaging satellite at geostationary orbit. Spacecraft Recovery & Remote Sensing, 39, 55-63(2018).

    [10] T Liu, R S Zhou. Development overview on GEO high resolution optical imaging system. Spacecraft Engineering, 26, 91-100(2017).

    [11] L J Meng, D Guo, M H Tang et al. Development status and prospect of high resolution imaging satellite in geostationary orbit. Spacecraft Recovery & Remote Sensing, 37, 1-6(2016).

    [12] T Yamashita, H Iwabuchi. Diurnal variations of different types of cloud over the Baiu-Meiyu frontal zone using retrieved cloud properties: Implication for the rainfall process. Atmospheric Research, 271, 106139(2022).

    [13] T Zhang. Research on Yiwu Precipitation Background Based on TBB(2009).

    [14] J Parikh. Cloud classification from visible and infrared SMS-1 data. Remote Sensing of Environment, 7, 85-92(1978).

    [15] G H Liu, X Yu, Z G Yue et al. Seeding conditions of precipitation enhancement revealed by multiple spectral data of satellite.II: Super-cooled layer clouds. Climatic and Environmental Research, 17, 758-766(2012).

    [16] Z J Cheng, H F Wang, J Bai. Review on ground-based sounding and retrieving of cloud microphysical parameters. Meteorological Science and Technology, 35, 9-14(2007).

    [17] Y Liu, B Wang, L Han. A review study of cloud classification using satellite imagery. Electronic Design Engineering, 19, 189-192(2011).

    [18] W R Chen. The Application of artificial neural networks in cloud classification: Its State-of-the-art and developing trend. Remote sensing technology and application, 66-73(1997).

    [19] R W Saunders, K T Kriebel. An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing, 9, 123-150(1988).

    [23] K Bessho, K J Date, M Hayashi et al. An introduction to Himawari-8/9—Japan's new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan Ser II, 94, 151-183(2016).

    [24] T J Schmit, M M Gunshor, W P Menzel et al. Introducing the next-generation advanced baseline imager on GOES-R. Bulletin of the American Meteorological Society, 86, 1079-1096(2005).

    [25] J Yang, Z Q Zhang, C Y Wei et al. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bulletin of the American Meteorological Society, 98, 1637-1658(2017).

    [26] P Zhang, L Zhu, S H Tang et al. General comparison of FY-4A/AGRI with other GEO/LEO instruments and its potential and challenges in non-meteorological applications. Frontiers in Earth Science, 6, 224(2019).

    [27] T C Gao, L Liu, S J Zhao et al. The actuality and progress of whole sky cloud sounding techniques. Journal of Applied Meteorological Science, 21, 101-109(2010).

    [28] E Ghasemifar, M Farajzadeh, Y Ghavidel Rahimi et al. Precipitation rate climatology related to different cloud types using satellite imagery over Iran. Arabian Journal of Geosciences, 11, 78(2018).

    [29] A Nespoli, A Niccolai, E Ogliari et al. Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery. Applied Energy, 305, 117834(2022).

    [31] W L Ju. Research on Cloud Mask and Cloud Classification Threshold Method Based on MTSAT Data(2013).

    [32] C Yang, Z H Yuan, S S Gu. Cloud classification of GMS-5 satellite imagery by the use of multispectral threshold technique. Transactions of Atmospheric Sciences, 25, 747-754(2002).

    [33] X Wu, R You, M Y Wang et al. Cloud type identification based on macro and micro properties of clouds from MODIS. Journal of Applied Meteorological Science, 27, 201-208(2016).

    [34] X J Zhou, X F Yang, X Z Yao. The study of cloud classification and detection in remote sensing image. Journal of Graphics, 35, 768-773(2014).

    [35] Y Huang, W J Zhang, F Yu. A method of cloud classification based on image entropy. Transactions of Atmospheric Sciences, 35, 633-639(2012).

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

    [37] Z G Liu, Y X Li, C Y Wang. An improved cloud classification method based on maximum likelihood for MODIS images. Journal of Computer Research and Development, 12-16(2007).

    [38] D P Y Suseno, T J Yamada. Two-dimensional, threshold-based cloud type classification using MTSAT data. Remote Sensing Letters, 3, 737-746(2012).

    [39] Y M Wang, F Yu. Experimental study on all-day inversion of main precipitation and non-precipitation clouds. Chinese Journal of Meteorology, 75, 618-631(2017).

    [40] J X Hu, D Rosenfeld, Y N Zhu et al. Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm. Atmospheric Research, 261, 105767(2021).

    [41] Y M Wu, R Zhang, G R Jiang et al. A fuzzy clustering method for multi-spectral satellite images. Journal of Tropical Meteorology, 20, 689-696(2004).

    [42] M Hong, R Zhang, Q L Wan et al. A combined cloud classification method of multi-spectral satellite cloud pictures based on GA and FCM. Progress in Geophysics, 20, 1009-1014(2005).

    [43] X Lai, G H Li, J Zhang. Satellite cloud images classification based on semi-supervised FCM method. Journal of National University of Defense Technology, 30, 73-77(2008).

    [44] C W Zhang. The Cloud-type Classification Research and Its Application for the New Generation Geostationary Satellite Himawari-8(2019).

    [45] Z F Yu, S Ma, D Han et al. A cloud classification method based on random forest for FY-4A. International Journal of Remote Sensing, 42, 3357-3383(2021).

    [46] K Wohlfarth, C Schröer, M Klaß et al. Dense cloud classification on multispectral satellite imagery, 1-6(2018).

    [47] C Chen. Cloud Classification of Satellite Imagery Based on ELM and SVM(2014).

    [48] W Z Tian, R D Fu, W Jin et al. Adaptive fuzzy support vector machine for classification of clouds in satellite imagery. Geomatics and Information Science of Wuhan University, 42, 488-495(2017).

    [49] C I Christodoulou, S C Michaelides, C S Pattichis. Multifeature texture analysis for the classification of clouds in satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 41, 2662-2668(2003).

    [50] T Y Yan. Research of FY-2E Night Satellite Cloud Images Classification Method Based on GHSOM Network Model(2015).

    [51] X Y Shi. Stationary satellite image classification based on SOM neural network. Electronic Design Engineering, 19, 55-56(2011).

    [52] Z H Zhang, C S Miao, Z H Zeng et al. Improvement and application of artificial neural networks to cloud classification. Journal of Applied Meteorological Science, 23, 355-363(2012).

    [53] K Y Cai, H Wang. Cloud classification of satellite image based on convolutional neural networks, 874-877(2017).

    [54] V Afzali Gorooh, S Kalia, P Nguyen et al. Deep neural network cloud-type classification (DeepCTC) model and its application in evaluating PERSIANN-CCS. Remote Sensing, 12, 316(2020).

    [55] D M Jiang, W M Chen, B S Fu et al. Automatic cloud classification based on radial basis function network. Journal of Nanjing University of Meteorology, 26, 89-95(2003).

    [56] Y Q Zheng, X F Yang, Z W Li. Detection of severe convective cloud over sea surface from geostationary meteorological satellite images based on deep learning. Journal of Remote Sensing, 24, 97-106(2020).

    [57] R D Fu, G Si, W Jin. Cloud classification based on ensemble learning combining with deep neural network and FSVM. Optics and Precision Engineering, 30, 917-927(2022).

    [58] B Huang, Y L Wang, R Zhang et al. A cloud classification model of multi-spectrum satellite cloud images based on the network coupling SOFM with PNN. Journal of Basic Science and Engineering, 16, 659-670(2008).

    [59] S Wang, C Y Xu, C X Shi et al. Study on cloud classification method of satellite cloud images based on CNN-LSTM. Computer Science, 49, 675-679, 783(2022).

    [60] H R Byers, H E Landsberg, H Wexler et al. On the physics of clouds and precipitation. Compendium of Meteorology: Prepared under the Direction of the Committee on the Compendium of Meteorology, 165-181(1951).

    [61] Y Y Li, L X Fang, X W Kou. Principle and standard of auto-observation cloud classification for satellite, ground measurements and model. Chinese Journal of Geophysics, 57, 2433-2441(2014).

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

    [63] X J Sun, L Liu, T C Gao et al. Classification of whole sky infrared cloud image based on the LBP operator. Transactions of Atmospheric Sciences, 32, 490-497(2009).

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

    [65] Q Trepte, P Minnis, R F Arduini. Daytime and nighttime polar cloud and snow identification using MODIS data, 4891, 449-459.

    [66] J Q Ren, W Yan, J Ye, D Han. Advances in the study of cloud phase discrimination using satellite remote sensing data. Advances in Earth Science, 25, 1051-1060(2010).

    [67] F Chéruy, F Aires. Cluster analysis of cloud properties over the southern European Mediterranean area in observations and a model. Monthly Weather Review, 137, 3161(2009).

    [68] U Amato, A Antoniadis, V Cuomo et al. Statistical cloud detection from SEVIRI multispectral images. Remote Sensing of Environment, 112, 750-766(2007).

    [69] I Van Zyl Marais, Preez J A Du, W H Steyn. An optimal image transform for threshold-based cloud detection using heteroscedastic discriminant analysis. International Journal of Remote Sensing, 32, 1713-1729(2011).

    [72] M R Azimi-Sadjadi, S A Zekavat. Cloud classification using support vector machines, 669-671(2000).

    [73] R T Liu. Ground-Based Cloud Images Classification Based on SVM-Classification-Tree(2012).

    [74] D Han, W Yan, J Q Ren et al. Cloud type classification algorithm for CloudSat satellite based on support vector machine. Transactions of Atmospheric Sciences, 34, 583-591(2011).

    [75] C W Zhang, X Y Zhuge, F Yu. Development of a high spatiotemporal resolution cloud-type classification approach using Himawari-8 and CloudSat. International Journal of Remote Sensing, 40, 6464-6481(2019).

    [76] B Tian, M A Shaikh, M R Azimi-Sadjadi et al. A study of cloud classification with neural networks using spectral and textural features. IEEE Transactions on Neural Networks, 10, 138-151(1999).

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

    [78] Z C Yu, Y Zhang, B X Yang et al. Scene level cloud classification algorithm based on convolutional neural network. Journal of Shenyang Normal University (Natural Science Edition), 37, 80-87(2019).

    [79] B Huang, L M Xiao, W Feng et al. Domain adaptation on multiple cloud recognition from different types of meteorological satellite. Frontiers in Earth Science, 10, 947032(2022).

    [80] S J Chen, C H Cheng, X Y Zhang et al. Construction of nighttime cloud layer height and classification of cloud types. Remote Sensing, 12, 668(2020).

    [81] F S Olesen, H Grassl. Cloud detection and classification over oceans at night with NOAA-7. International Journal of Remote Sensing, 6, 1435-1444(1985).

    [82] W B Zhang, J W Wang, D Jin et al. A deterministic self-organizing map approach and its application on satellite data based cloud type classification, 2027-2034(2018).

    [83] T Y Yan, S Wang. The GHSOM network cloud classification model of stationary satellite infrared cloud images at night. Journal of Jiangxi Normal University (Natural Science Edition), 39, 383-388+410(2015).

    [84] Z H Tan, C Liu, S Ma et al. Detecting multilayer clouds from the geostationary advanced Himawari imager using machine learning techniques. IEEE Transactions on Geoscience and Remote Sensing, 60, 4103112(2022).

    [85] W W Li, F Zhang, H Lin et al. Cloud detection and classification algorithms for Himawari-8 imager measurements based on deep learning. IEEE Transactions on Geoscience and Remote Sensing, 60, 4107117(2022).

    [86] Z Zhang, D H Li, S Liu. Salient dual activations aggregation for ground-based cloud classification in weather station networks. IEEE Access, 6, 59173-59181(2018).

    [87] M Toğaçar, B Ergen. Classification of cloud images by using super resolution, semantic segmentation approaches and binary sailfish optimization method with deep learning model. Computers and Electronics in Agriculture, 193, 106724(2022).

    Yashuai FU, Wenhao ZHANG, Yongtao JIN, Qiyue LIU, Lili ZHANG, Fangfei BING, Yu MA. Research progress of cloud classification based on optical satellite remote sensing[J]. Journal of Atmospheric and Environmental Optics, 2025, 20(1): 1
    Download Citation