Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning
Classification of Ice Crystal Habits Observed From Airborne Cloud Particle Imager by Deep Transfer Learning
复制标题
通过深度迁移学习对机载云粒子成像仪观察到的冰晶习性进行分类
DOI：
10.1029/2019ea000636
复制
发表时间：
2019-10
影响因子：
3.1
通讯作者：
Qianshan He
中科院分区：
文献类型：
--
作者：
Haixia Xiao;Feng Zhang;Qianshan He
冰云大多由不同的冰晶习性组成。对冰晶习惯进行分类是非常重要的，因为它们可能会产生很大的影响。冰晶颗粒的单次散射特性。单次散射特性在云遥感和地球大气辐射收支研究中起着重要的作用。然而，冰云中蕴藏着无数形状各异的冰晶，基于肉眼观测的经验分类任务不可靠、费时、主观，导致分类结果具有明显的不确定性和偏差。本文利用中国机载云粒成像仪观测到的冰晶图像，建立了中国冰晶数据库，它由10个习惯类别组成，包含7000多幅图像。提出了一种冰晶习性自动分类模型TL-ResNet152，它是一种基于迁移学习方法的深度卷积神经网络。结果表明，TL-ResNet152模型对冰晶习性的分类性能可靠，分类正确率达96%，远远高于传统的分类方法。实现冰晶习性的高精度自动分类，有助于我们更好地了解冰云的辐射特性。
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 ncertainties 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.
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DOI：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
--
作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
影响因子：
3.8
作者：
R. Okamura;H. Iwabuchi;K. S. Schmidt
通讯作者：
R. Okamura;H. Iwabuchi;K. S. Schmidt
DOI：
--
发表时间：
2016-02
期刊：
ArXiv
影响因子：
--
作者：
F. Iandola;Matthew W. Moskewicz;Khalid Ashraf;Song Han;W. Dally;K. Keutzer
通讯作者：
F. Iandola;Matthew W. Moskewicz;Khalid Ashraf;Song Han;W. Dally;K. Keutzer
DOI：
10.1088/0031-9112/31/5/037
发表时间：
1980
期刊：
Physics Bulletin
影响因子：
--
作者：
J. Gilmour
通讯作者：
J. Gilmour
DOI：
--
发表时间：
2016
期刊：
--
影响因子：
--
作者：
F. Iandola;Song Han;Matthew W. Moskewicz;Khalid Ashraf;W. Dally;K. Keutzer
通讯作者：
F. Iandola;Song Han;Matthew W. Moskewicz;Khalid Ashraf;W. Dally;K. Keutzer