Contrastive Learning Ideas in Underwater Terrain Image Matching

The distinctive nature of the intrinsic features of underwater terrain images, along with their variability of intensity and texture due to the complex environment in which they are acquired, poses great challenges to manual feature-based template-matching methods. In this paper, we propose a novel...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-1
Hauptverfasser:	Zhang, Feng, Bian, HongYu, Wei, MingZhe
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation Annotations Competitors Contrastive learning Feature extraction Gray-scale Image enhancement Image matching Learning Methods Noise measurement patch-based sample extraction Stochastic processes Task analysis Template matching Templates Terrain Texture Training Underwater underwater terrain image
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