Machine-learning-based classification and spatiotemporal analysis of sea ice in the Bohai Sea

Wenhui Wang; Rongpei Wang

doi:10.1117/12.3029236

16 May 2024 Machine-learning-based classification and spatiotemporal analysis of sea ice in the Bohai Sea

Wenhui Wang, Rongpei Wang

Proceedings Volume 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024); 131660P (2024) https://doi.org/10.1117/12.3029236
Event: International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 2024, Changchun, China

Abstract

In recent years, frequent sea ice disasters have led to severe disruptions in marine ecology and hindered maritime transportation. In this study, Sentinel-1 data from 2016 to 2022 were utilized to extract texture features from images. A comparative analysis of the classification accuracy of three models—Support Vector Machine, Iterative Self-Organizing Clustering, and Random Forest—was conducted. A high-precision sea ice classification model was established to analyze spatiotemporal changes. The results indicate that the Support Vector Machine model exhibited the highest accuracy, with an overall accuracy of 87.61% and a kappa coefficient of 81.42%, demonstrating the model's stability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenhui Wang and Rongpei Wang "Machine-learning-based classification and spatiotemporal analysis of sea ice in the Bohai Sea", Proc. SPIE 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 131660P (16 May 2024); https://doi.org/10.1117/12.3029236

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KEYWORDS

Ice

Support vector machines

Image classification

Random forests

Machine learning

Remote sensing

Synthetic aperture radar

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