Sea-Ice Classification Using Conditional Generative Adversarial Networks

Alsharay, Nahed; Dobre, Octavia A.; Chen, Yuanzhu; Silva, Oscar De

doi:10.1109/lsens.2023.3259202

Cited by 2 publications

(1 citation statement)

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“…In [132], considering the impact of raindrops on the segmentation results of captured images, raindrop removal techniques were developed to improve the classification performance. In [133], a semantic segmentation model based on a conditional generative adversarial network (cGAN) was proposed. This model has good robustness and makes the effect of raindrops on the segmentation results smaller.…”

Section: Supervised Learningmentioning

confidence: 99%

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

Huang,

Yu,

et al. 2024

Remote Sensing

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Deep learning, which is a dominating technique in artificial intelligence, has completely changed 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 future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the 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 four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions.

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Section: Supervised Learningmentioning

confidence: 99%