The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.
@article{arxiv.2306.00303,
title = {Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges},
author = {Anzhu Yu and Wenjun Huang and Qing Xu and Qun Sun and Wenyue Guo and Song Ji and Bowei Wen and Chunping Qiu},
journal= {arXiv preprint arXiv:2306.00303},
year = {2023}
}