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

• Main Authors: Yu, Anzhu, Huang, Wenjun, Xu, Qing, Sun, Qun, Guo, Wenyue, Ji, Song, Wen, Bowei, Qiu, Chunping
• Format: Report
• Language: unknown
• Published: arXiv 2023
• Subjects: Computer Vision and Pattern Recognition cs.CV; Image and Video Processing eess.IV; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2306.00303; https://arxiv.org/abs/2306.00303

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. ... : 24 pages, 6 figures ...