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arXiv · 2306.00303

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

Anzhu Yu·Wenjun Huang·Qing Xu·Qun Sun·Wenyue Guo·Song Ji·Bowei Wen·Chunping Qiu
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Abstract

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.

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Anzhu Yu, Wenjun Huang, Qing Xu, Qun Sun, Wenyue Guo, Song Ji, Bowei Wen, Chunping Qiu. 2023-06-01. Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges. https://arxiv.org/abs/2306.00303

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