Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning. Issue 10 (16th October 2019)

• Record Type: Journal Article
• Authors: Xiao, Haixia; Zhang, Feng; He, Qianshan; Liu, Pu; Yan, Fei; Miao, Lijuan; Yang, Zhipeng
• Is Part Of: Earth and space science. Volume 6:Issue 10(2019)
• Page Start: 1877
• Page End: 1886
• Publication Date: 2019-10-16
• Subjects: classification -- ice crystals -- deep convolutional neural network -- ice clouds -- transfer learning; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2019EA000636
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single‐scattering properties of ice crystal particles. The single‐scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked‐eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7, 000 images. We propose an automatic classification model of ice crystal habits, called TL‐ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL‐ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high‐precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds. Plain Language Summary: In recent years, Convolutional Neural Networks (CNNs), as one of the representative algorithms used in deep learning, have been widely applied in the field of image classification and have achieved remarkable results. However, to the best of our knowledge, there are few studies regarding the application of deep CNNs to ice crystal image classification. In this paper, we propose an automatic classification model of ice crystal habits called TL‐ResNet152, which is a deep CNN based on a newly developed method of transfer learning. Using the TL‐ResNet152 model, high precision and automatic classification of ice crystal habits could be achieved, furthering our understanding of radiation characteristics of ice clouds. We have set up an ice crystal data set, called Ice Crystals Database in China, which consists of 10 habit categories with 7, 282 images. As far as we know, it is the first publicly available ice crystals data set in China, pertaining to ice crystals observed in natural ice clouds. The publication of this database will promote more and more researches into understanding the physical process of ice clouds based on ice crystal habits classification. Key Points: An ice crystal data set called Ice Crystals Database in China (ICDC) containing over 7, 000 images has been first established We propose an automatic classification model of ice crystal habits, TL‐ResNet152, which is a convolutional neural network created using transfer learning The high‐precision automatic classification of ice crystal habits is achieved by using this classification model