Voting in Transfer Learning System for Ground-Based Cloud Classification
Cloud classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact the variation of atmospheric conditions, with consequent strong dominance over the earth’s climate and weather. T...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/3/3/28 |
| _version_ | 1797518461683892224 |
|---|---|
| author | Mario Manzo Simone Pellino |
| author_facet | Mario Manzo Simone Pellino |
| author_sort | Mario Manzo |
| collection | DOAJ |
| description | Cloud classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact the variation of atmospheric conditions, with consequent strong dominance over the earth’s climate and weather. Therefore, identifying their main visual features becomes a crucial aspect. In this paper, the goal is to adopt pretrained deep neural networks-based architecture for clouds image description, and subsequently, classification. The approach is pyramidal. Proceeding from the bottom up, it partially extracts previous knowledge of deep neural networks related to original task and transfers it to the new task. The updated knowledge is integrated in a voting context to provide a classification prediction. The framework trains the neural models on unbalanced sets, a condition that makes the task even more complex, and combines the provided predictions through statistical measures. An experimental phase on different cloud image datasets is performed, and the results achieved show the effectiveness of the proposed approach with respect to state-of-the-art competitors. |
| first_indexed | 2024-03-10T07:30:13Z |
| format | Article |
| id | doaj.art-aabdd1ba29d646299cd3dea10a56086c |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-10T07:30:13Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-aabdd1ba29d646299cd3dea10a56086c2023-11-22T13:58:10ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902021-07-013354255310.3390/make3030028Voting in Transfer Learning System for Ground-Based Cloud ClassificationMario Manzo0Simone Pellino1Information Technology Services, University of Naples “L’Orientale”, 80121 Naples, ItalyDepartment of Applied Science, I.S. Mattei Aversa M.I.U.R., 81031 Rome, ItalyCloud classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact the variation of atmospheric conditions, with consequent strong dominance over the earth’s climate and weather. Therefore, identifying their main visual features becomes a crucial aspect. In this paper, the goal is to adopt pretrained deep neural networks-based architecture for clouds image description, and subsequently, classification. The approach is pyramidal. Proceeding from the bottom up, it partially extracts previous knowledge of deep neural networks related to original task and transfers it to the new task. The updated knowledge is integrated in a voting context to provide a classification prediction. The framework trains the neural models on unbalanced sets, a condition that makes the task even more complex, and combines the provided predictions through statistical measures. An experimental phase on different cloud image datasets is performed, and the results achieved show the effectiveness of the proposed approach with respect to state-of-the-art competitors.https://www.mdpi.com/2504-4990/3/3/28cloud classificationdeep learningtransfer learningvoting-based classificationclimate change |
| spellingShingle | Mario Manzo Simone Pellino Voting in Transfer Learning System for Ground-Based Cloud Classification Machine Learning and Knowledge Extraction cloud classification deep learning transfer learning voting-based classification climate change |
| title | Voting in Transfer Learning System for Ground-Based Cloud Classification |
| title_full | Voting in Transfer Learning System for Ground-Based Cloud Classification |
| title_fullStr | Voting in Transfer Learning System for Ground-Based Cloud Classification |
| title_full_unstemmed | Voting in Transfer Learning System for Ground-Based Cloud Classification |
| title_short | Voting in Transfer Learning System for Ground-Based Cloud Classification |
| title_sort | voting in transfer learning system for ground based cloud classification |
| topic | cloud classification deep learning transfer learning voting-based classification climate change |
| url | https://www.mdpi.com/2504-4990/3/3/28 |
| work_keys_str_mv | AT mariomanzo votingintransferlearningsystemforgroundbasedcloudclassification AT simonepellino votingintransferlearningsystemforgroundbasedcloudclassification |