Back to Search
Start Over
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach.
- Source :
-
Cryosphere . 2023, Vol. 17 Issue 12, p5519-5537. 19p. - Publication Year :
- 2023
-
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. A most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data, where the segmentation of individual ice floes is a challenge in obtaining FSD from remotely sensed images. In this study, we adopt a deep learning (DL) semantic segmentation network to fast and adaptive implement the task of ice floe instance segmentation. In order to alleviate the costly and time-consuming data annotation problem of model training, classical image processing technique is applied to automatically label ice floes in local-scale marginal ice zone (MIZ). Several state-of-the-art (SoA) semantic segmentation models are then trained on the labelled MIZ dataset and further applied to additional large-scale optical Sentinel-2 images to evaluate their performance in floe instance segmentation and to determine the best model. 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 to derive acceptable FSDs at local and global scales. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19940416
- Volume :
- 17
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Cryosphere
- Publication Type :
- Academic Journal
- Accession number :
- 174638028
- Full Text :
- https://doi.org/10.5194/tc-17-5519-2023