Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges
This review addresses the problem of extracting sea ice data for researchers and practitioners in climate science, navigation, and GIS, but it is incremental as it synthesizes existing work without new results.
The paper provides a comprehensive review of sea ice extraction (SIE) from remote sensed imagery, covering algorithms, datasets, applications, and future trends, with a focus on deep learning-based approaches from 2016 onward.
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.