Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera
A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six p...
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ftdoajarticles:oai:doaj.org/article:59b2e1153819459c9511f8f81975cf75 2023-05-15T13:46:45+02:00 Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera C. Praz Y.-A. Roulet A. Berne 2017-04-01T00:00:00Z https://doi.org/10.5194/amt-10-1335-2017 https://doaj.org/article/59b2e1153819459c9511f8f81975cf75 EN eng Copernicus Publications http://www.atmos-meas-tech.net/10/1335/2017/amt-10-1335-2017.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 1867-1381 1867-8548 doi:10.5194/amt-10-1335-2017 https://doaj.org/article/59b2e1153819459c9511f8f81975cf75 Atmospheric Measurement Techniques, Vol 10, Iss 4, Pp 1335-1357 (2017) Environmental engineering TA170-171 Earthwork. Foundations TA715-787 article 2017 ftdoajarticles https://doi.org/10.5194/amt-10-1335-2017 2022-12-31T01:55:02Z A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent. Article in Journal/Newspaper Antarc* Antarctica Directory of Open Access Journals: DOAJ Articles Atmospheric Measurement Techniques 10 4 1335 1357 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
spellingShingle |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 C. Praz Y.-A. Roulet A. Berne Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera |
topic_facet |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
description |
A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent. |
format |
Article in Journal/Newspaper |
author |
C. Praz Y.-A. Roulet A. Berne |
author_facet |
C. Praz Y.-A. Roulet A. Berne |
author_sort |
C. Praz |
title |
Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera |
title_short |
Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera |
title_full |
Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera |
title_fullStr |
Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera |
title_full_unstemmed |
Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera |
title_sort |
solid hydrometeor classification and riming degree estimation from pictures collected with a multi-angle snowflake camera |
publisher |
Copernicus Publications |
publishDate |
2017 |
url |
https://doi.org/10.5194/amt-10-1335-2017 https://doaj.org/article/59b2e1153819459c9511f8f81975cf75 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_source |
Atmospheric Measurement Techniques, Vol 10, Iss 4, Pp 1335-1357 (2017) |
op_relation |
http://www.atmos-meas-tech.net/10/1335/2017/amt-10-1335-2017.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 1867-1381 1867-8548 doi:10.5194/amt-10-1335-2017 https://doaj.org/article/59b2e1153819459c9511f8f81975cf75 |
op_doi |
https://doi.org/10.5194/amt-10-1335-2017 |
container_title |
Atmospheric Measurement Techniques |
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10 |
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4 |
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1335 |
op_container_end_page |
1357 |
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1766245168511451136 |