From pixels to patches: a cloud classification method based on a bag of micro-structures
Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-03-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | http://www.atmos-meas-tech.net/9/753/2016/amt-9-753-2016.pdf |
| _version_ | 1818294131141640192 |
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| author | Q. Li Z. Zhang W. Lu J. Yang Y. Ma W. Yao |
| author_facet | Q. Li Z. Zhang W. Lu J. Yang Y. Ma W. Yao |
| author_sort | Q. Li |
| collection | DOAJ |
| description | Automatic cloud classification has attracted more and more attention with the
increasing development of whole sky imagers, but it is still in progress for
ground-based cloud observation. This paper proposes a new cloud
classification method, named bag of micro-structures (BoMS). This method
treats an all-sky image as a collection of micro-structures mapped from image
patches, rather than a collection of pixels. It represents the image with
a weighted histogram of micro-structures. Based on this representation, BoMS
recognizes the cloud class of the image by a support vector machine (SVM)
classifier. Five classes of sky condition are identified: cirriform,
cumuliform, stratiform, clear sky, and mixed cloudiness. BoMS is evaluated on
a large data set, which contains 5000 all-sky images captured by a total-sky
cloud imager located in Tibet (29.25° N, 88.88° E). BoMS
achieves an accuracy of 90.9 % for 10-fold cross-validation, and it
outperforms state-of-the-art methods with an increase of 19 %.
Furthermore, influence of key parameters in BoMS is investigated to verify
their robustness. |
| first_indexed | 2024-12-13T03:26:52Z |
| format | Article |
| id | doaj.art-c2c4773353f34d3480d9b752fcc891ce |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-13T03:26:52Z |
| publishDate | 2016-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-c2c4773353f34d3480d9b752fcc891ce2022-12-22T00:01:16ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482016-03-019275376410.5194/amt-9-753-2016From pixels to patches: a cloud classification method based on a bag of micro-structuresQ. Li0Z. Zhang1W. Lu2J. Yang3Y. Ma4W. Yao5Beijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, ChinaBeijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, ChinaAutomatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. It represents the image with a weighted histogram of micro-structures. Based on this representation, BoMS recognizes the cloud class of the image by a support vector machine (SVM) classifier. Five classes of sky condition are identified: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness. BoMS is evaluated on a large data set, which contains 5000 all-sky images captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10-fold cross-validation, and it outperforms state-of-the-art methods with an increase of 19 %. Furthermore, influence of key parameters in BoMS is investigated to verify their robustness.http://www.atmos-meas-tech.net/9/753/2016/amt-9-753-2016.pdf |
| spellingShingle | Q. Li Z. Zhang W. Lu J. Yang Y. Ma W. Yao From pixels to patches: a cloud classification method based on a bag of micro-structures Atmospheric Measurement Techniques |
| title | From pixels to patches: a cloud classification method based on a bag of micro-structures |
| title_full | From pixels to patches: a cloud classification method based on a bag of micro-structures |
| title_fullStr | From pixels to patches: a cloud classification method based on a bag of micro-structures |
| title_full_unstemmed | From pixels to patches: a cloud classification method based on a bag of micro-structures |
| title_short | From pixels to patches: a cloud classification method based on a bag of micro-structures |
| title_sort | from pixels to patches a cloud classification method based on a bag of micro structures |
| url | http://www.atmos-meas-tech.net/9/753/2016/amt-9-753-2016.pdf |
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