Label Smoothing Technique for Ordinal Classification in Cloud Assessment

Yuxuan Wei; Qixuan Liu; Guixu Zhang; Yaxin Peng; Chaomin Shen

doi:10.1109/IGARSS39084.2020.9323714

Label Smoothing Technique for Ordinal Classification in Cloud Assessment

Yuxuan Wei, Qixuan Liu, Guixu Zhang, Yaxin Peng, Chaomin Shen

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Satellite image classification is a challenging task if the input labels are not sufficiently accurate. The automatic cloud cover assessment (ACCA), for example, aims to classify the cloud covers of satellite images as alphabetical categories from A to E showing the escalating levels of clouds; however, those labels for training are often obtained by a subjective qualitative assessment, i.e., they may be not accurate. Therefore, this paper studies how to conduct ACCA under this circumstance. We propose a label smoothing approach and improve the accuracy around 3 percentage points (e.g., from 75.9% to 78.4% for ResNet network) without changing other network structures and parameters.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2264-2267
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9323714
State	Published - 26 Sep 2020
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: 26 Sep 2020 → 2 Oct 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	26/09/20 → 2/10/20

Keywords

Ordinary classification
cloud cover assessment
label smoothing
loss function
neural network

Access to Document

10.1109/IGARSS39084.2020.9323714

Cite this

Wei, Y., Liu, Q., Zhang, G., Peng, Y., & Shen, C. (2020). Label Smoothing Technique for Ordinal Classification in Cloud Assessment. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 2264-2267). Article 9323714 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9323714

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title = "Label Smoothing Technique for Ordinal Classification in Cloud Assessment",

abstract = "Satellite image classification is a challenging task if the input labels are not sufficiently accurate. The automatic cloud cover assessment (ACCA), for example, aims to classify the cloud covers of satellite images as alphabetical categories from A to E showing the escalating levels of clouds; however, those labels for training are often obtained by a subjective qualitative assessment, i.e., they may be not accurate. Therefore, this paper studies how to conduct ACCA under this circumstance. We propose a label smoothing approach and improve the accuracy around 3 percentage points (e.g., from 75.9\% to 78.4\% for ResNet network) without changing other network structures and parameters.",

keywords = "Ordinary classification, cloud cover assessment, label smoothing, loss function, neural network",

author = "Yuxuan Wei and Qixuan Liu and Guixu Zhang and Yaxin Peng and Chaomin Shen",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",

year = "2020",

month = sep,

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doi = "10.1109/IGARSS39084.2020.9323714",

language = "英语",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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pages = "2264--2267",

booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings",

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Wei, Y, Liu, Q, Zhang, G, Peng, Y & Shen, C 2020, Label Smoothing Technique for Ordinal Classification in Cloud Assessment. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9323714, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 2264-2267, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 26/09/20. https://doi.org/10.1109/IGARSS39084.2020.9323714

Label Smoothing Technique for Ordinal Classification in Cloud Assessment. / Wei, Yuxuan; Liu, Qixuan; Zhang, Guixu et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2264-2267 9323714 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Label Smoothing Technique for Ordinal Classification in Cloud Assessment

AU - Wei, Yuxuan

AU - Liu, Qixuan

AU - Zhang, Guixu

AU - Peng, Yaxin

AU - Shen, Chaomin

PY - 2020/9/26

Y1 - 2020/9/26

N2 - Satellite image classification is a challenging task if the input labels are not sufficiently accurate. The automatic cloud cover assessment (ACCA), for example, aims to classify the cloud covers of satellite images as alphabetical categories from A to E showing the escalating levels of clouds; however, those labels for training are often obtained by a subjective qualitative assessment, i.e., they may be not accurate. Therefore, this paper studies how to conduct ACCA under this circumstance. We propose a label smoothing approach and improve the accuracy around 3 percentage points (e.g., from 75.9% to 78.4% for ResNet network) without changing other network structures and parameters.

AB - Satellite image classification is a challenging task if the input labels are not sufficiently accurate. The automatic cloud cover assessment (ACCA), for example, aims to classify the cloud covers of satellite images as alphabetical categories from A to E showing the escalating levels of clouds; however, those labels for training are often obtained by a subjective qualitative assessment, i.e., they may be not accurate. Therefore, this paper studies how to conduct ACCA under this circumstance. We propose a label smoothing approach and improve the accuracy around 3 percentage points (e.g., from 75.9% to 78.4% for ResNet network) without changing other network structures and parameters.

KW - Ordinary classification

KW - cloud cover assessment

KW - label smoothing

KW - loss function

KW - neural network

UR - https://www.scopus.com/pages/publications/85101970578

U2 - 10.1109/IGARSS39084.2020.9323714

DO - 10.1109/IGARSS39084.2020.9323714

M3 - 会议稿件

AN - SCOPUS:85101970578

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 2264

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BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020

Y2 - 26 September 2020 through 2 October 2020

ER -

Wei Y, Liu Q, Zhang G, Peng Y, Shen C. Label Smoothing Technique for Ordinal Classification in Cloud Assessment. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 2264-2267. 9323714. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9323714

Label Smoothing Technique for Ordinal Classification in Cloud Assessment

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