Comment on egusphere-2023-295

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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.