A convolutional neural network for classifying cloud particles recorded by imaging probes
<p>During typical field campaigns, millions of cloud particle images are captured with imaging probes. Our interest lies in classifying these particles in order to compute the statistics needed for understanding clouds. Given the large volume of collected data, this raises the need for an auto...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-05-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/13/2219/2020/amt-13-2219-2020.pdf |
| _version_ | 1819225846636871680 |
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| author | G. Touloupas A. Lauber J. Henneberger A. Beck A. Lucchi |
| author_facet | G. Touloupas A. Lauber J. Henneberger A. Beck A. Lucchi |
| author_sort | G. Touloupas |
| collection | DOAJ |
| description | <p>During typical field campaigns, millions of cloud particle images are captured with imaging probes. Our interest lies in classifying these particles in order to compute the statistics needed for understanding clouds. Given the large volume of collected data, this raises the need for an automated classification approach. Traditional classification methods that require extracting features manually (e.g., decision trees and support vector machines) show reasonable performance when trained and tested on data coming from a unique dataset. However, they often have difficulties in generalizing to test sets coming from other datasets where the distribution of the features might be significantly different. In practice, we found that for holographic imagers each new dataset requires labeling a huge amount of data by hand using those methods. Convolutional neural networks have the potential to overcome this problem due to their ability to learn complex nonlinear models directly from the images instead of pre-engineered features, as well as by relying on powerful regularization techniques. We show empirically that a convolutional neural network trained on cloud particles from holographic imagers generalizes well to unseen datasets. Moreover, fine tuning the same network with a small number (256) of training images improves the classification accuracy. Thus, the automated classification with a convolutional neural network not only reduces the hand-labeling effort for new datasets but is also no longer the main error source for the classification of small particles.</p> |
| first_indexed | 2024-12-23T10:16:05Z |
| format | Article |
| id | doaj.art-d2b4e6be94dc4f23b394fad24b50b5d4 |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-23T10:16:05Z |
| publishDate | 2020-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-d2b4e6be94dc4f23b394fad24b50b5d42022-12-21T17:50:49ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482020-05-01132219223910.5194/amt-13-2219-2020A convolutional neural network for classifying cloud particles recorded by imaging probesG. Touloupas0A. Lauber1J. Henneberger2A. Beck3A. Lucchi4Institute for Machine Learning, ETH Zurich, Zurich, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandInstitute for Machine Learning, ETH Zurich, Zurich, Switzerland<p>During typical field campaigns, millions of cloud particle images are captured with imaging probes. Our interest lies in classifying these particles in order to compute the statistics needed for understanding clouds. Given the large volume of collected data, this raises the need for an automated classification approach. Traditional classification methods that require extracting features manually (e.g., decision trees and support vector machines) show reasonable performance when trained and tested on data coming from a unique dataset. However, they often have difficulties in generalizing to test sets coming from other datasets where the distribution of the features might be significantly different. In practice, we found that for holographic imagers each new dataset requires labeling a huge amount of data by hand using those methods. Convolutional neural networks have the potential to overcome this problem due to their ability to learn complex nonlinear models directly from the images instead of pre-engineered features, as well as by relying on powerful regularization techniques. We show empirically that a convolutional neural network trained on cloud particles from holographic imagers generalizes well to unseen datasets. Moreover, fine tuning the same network with a small number (256) of training images improves the classification accuracy. Thus, the automated classification with a convolutional neural network not only reduces the hand-labeling effort for new datasets but is also no longer the main error source for the classification of small particles.</p>https://www.atmos-meas-tech.net/13/2219/2020/amt-13-2219-2020.pdf |
| spellingShingle | G. Touloupas A. Lauber J. Henneberger A. Beck A. Lucchi A convolutional neural network for classifying cloud particles recorded by imaging probes Atmospheric Measurement Techniques |
| title | A convolutional neural network for classifying cloud particles recorded by imaging probes |
| title_full | A convolutional neural network for classifying cloud particles recorded by imaging probes |
| title_fullStr | A convolutional neural network for classifying cloud particles recorded by imaging probes |
| title_full_unstemmed | A convolutional neural network for classifying cloud particles recorded by imaging probes |
| title_short | A convolutional neural network for classifying cloud particles recorded by imaging probes |
| title_sort | convolutional neural network for classifying cloud particles recorded by imaging probes |
| url | https://www.atmos-meas-tech.net/13/2219/2020/amt-13-2219-2020.pdf |
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