Cloud classification of ground-based infrared images combining manifold and texture features

Luo, Qixiang; Meng, Yong; Liu, Lei; Zhao, Xiaofeng; Zhou, Zeming

doi:https://doi.org/10.5194/amt-11-5351-2018

Articles | Volume 11, issue 9

https://doi.org/10.5194/amt-11-5351-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-11-5351-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 9

Research article

|

25 Sep 2018

Research article |

| 25 Sep 2018

Cloud classification of ground-based infrared images combining manifold and texture features

Qixiang Luo, Yong Meng, Lei Liu, Xiaofeng Zhao, and Zeming Zhou

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Final revised paper (published on 25 Sep 2018)
Preprint (discussion started on 22 Dec 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Cloud classification of ground-based infrared images combining manifold and texture features', Roger Clay, 15 Feb 2018
- AC1: 'Response to Clay comment', Qixiang Luo, 02 Mar 2018
- AC4: 'Revised manuscript', Qixiang Luo, 09 Aug 2018
RC2: 'Referee coment', Anonymous Referee #2, 16 Jul 2018
- AC2: 'Response to referee 2', Qixiang Luo, 09 Aug 2018
- AC3: 'Revised manuscript', Qixiang Luo, 09 Aug 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Qixiang Luo on behalf of the Authors (10 Aug 2018) Author's response Manuscript

ED: Publish subject to technical corrections (07 Sep 2018) by Alyn Lambert

AR by Qixiang Luo on behalf of the Authors (07 Sep 2018) Author's response Manuscript

Short summary

In this paper, a novel cloud classification method is proposed to group images into five cloud types based on manifold and texture features. The proposed method is comprised of three stages: data pre-processing, feature extraction and classification. Compared to the recent cloud type recognition methods, the experimental results illustrate that the proposed method acquires a higher recognition rate with an increase of 2%–10% on the ground-based infrared datasets.