Preprints
https://doi.org/10.5194/egusphere-2023-295
https://doi.org/10.5194/egusphere-2023-295
03 Mar 2023
 | 03 Mar 2023

Towards a manual-free labelling approach for deep learning-based ice floe instance segmentation in airborne and high-resolution optical satellite images

Qin Zhang and Nick Hughes

Abstract. Floe size distribution (FSD) has become a parameter of great interest in observations of sea ice because of its importance in affecting climate change, marine ecosystems, and human activities in the polar ocean. The sizes of ice floes can range from less than a square metre to hundreds of square kilometres, so the most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data. The segmentation of individual ice floes is crucial for obtaining FSD from remotely sensed images, and it is a challenge to separate floes that appear to be connected. Although deep learning (DL) networks have achieved great success in image processing, they still have limitations in this application. A key reason is the lack of sufficient labelled data, which is costly and time-consuming to produce. In order to alleviate this issue, we use classical image processing techniques to achieve a manual-label free ice floe image annotation, which is further used to train DL models for fast and adaptive individual ice floe segmentation, especially for separating visibly connected floes. A post-processing algorithm is also proposed in our work to refine the segmentation. Our approach has been applied to both airborne and high-resolution optical (HRO) satellite images, and successfully derived FSD at local and global scales.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

Journal article(s) based on this preprint

22 Dec 2023
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023,https://doi.org/10.5194/tc-17-5519-2023, 2023
Short summary
Qin Zhang and Nick Hughes

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-295', Anonymous Referee #1, 30 Mar 2023
  • RC2: 'Comment on egusphere-2023-295', Anonymous Referee #2, 14 Apr 2023
  • RC3: 'Comment on egusphere-2023-295', Anonymous Referee #3, 08 May 2023
  • EC1: 'Comment on egusphere-2023-295', Bin Cheng, 15 May 2023
    • AC4: 'Reply on EC1', Qin Zhang, 11 Jun 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-295', Anonymous Referee #1, 30 Mar 2023
  • RC2: 'Comment on egusphere-2023-295', Anonymous Referee #2, 14 Apr 2023
  • RC3: 'Comment on egusphere-2023-295', Anonymous Referee #3, 08 May 2023
  • EC1: 'Comment on egusphere-2023-295', Bin Cheng, 15 May 2023
    • AC4: 'Reply on EC1', Qin Zhang, 11 Jun 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (16 Jun 2023) by Bin Cheng
AR by Qin Zhang on behalf of the Authors (30 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Oct 2023) by Bin Cheng
RR by Anonymous Referee #1 (16 Oct 2023)
RR by Anonymous Referee #2 (18 Oct 2023)
ED: Publish subject to minor revisions (review by editor) (22 Oct 2023) by Bin Cheng
AR by Qin Zhang on behalf of the Authors (30 Oct 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (08 Nov 2023) by Bin Cheng
AR by Qin Zhang on behalf of the Authors (17 Nov 2023)  Manuscript 

Journal article(s) based on this preprint

22 Dec 2023
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023,https://doi.org/10.5194/tc-17-5519-2023, 2023
Short summary
Qin Zhang and Nick Hughes
Qin Zhang and Nick Hughes

Viewed

Total article views: 688 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
437 218 33 688 18 19
  • HTML: 437
  • PDF: 218
  • XML: 33
  • Total: 688
  • BibTeX: 18
  • EndNote: 19
Views and downloads (calculated since 03 Mar 2023)
Cumulative views and downloads (calculated since 03 Mar 2023)

Viewed (geographical distribution)

Total article views: 678 (including HTML, PDF, and XML) Thereof 678 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Sep 2024
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Ice floes image annotation is the key to training deep learning (DL) models to extract individual floes from images. But manually labeling each floe in images is a tedious work. To alleviate this, we first develop an automatic approach to annotate floe images without manual intervention. We then apply DL method for fast and adaptive floe instance segmentation for airborne and high-resolution satellite images, and have successfully derived floe size distributions at local and global scales.