Classification of ice crystal habits observed from airborne Cloud Particle Imager by deep transfer learning - Haixia Xiao; Feng Zhang; Qianshan He; Pu Liu; Fei Yan; Lijuan Miao; Zhipeng Yang (Earth and Space Science, 2019)
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.
Keywords: 330; classification; ice crystals; deep convolutional neural network; ice clouds; transfer learning