Application of adaptive weight fusion algorithm to wetland classification in Yellow River Delta

Xinyue Zhao; Yingdan Wu; Yanxi Lan; Yibei Guo; Yanglin Shao; Zirui Hu

doi:10.1117/12.2667727

10 February 2023 Application of adaptive weight fusion algorithm to wetland classification in Yellow River Delta

Xinyue Zhao, Yingdan Wu, Yanxi Lan, Yibei Guo, Yanglin Shao, Zirui Hu

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522R (2023) https://doi.org/10.1117/12.2667727
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

The Yellow River Delta wetland is the largest estuarine delta wetland in China. Identifying and classifying the Yellow River Delta wetlands by remote sensing is of great importance to promoting the monitoring and sustainable development of the Yellow River Delta wetland. In this paper, an ensemble algorithm based on an adaptive weight fusion strategy was proposed to identify wetland types in the Yellow River Delta based on Sentinel-2 data, DEM data, and the constructed vegetation and water index. The experimental results show that the classification performance of the ensemble algorithm using the weight fusion strategy outperforms that of a single machine learning classifier, with an average improvement of 9.88% in overall accuracy (OA) and 11.04% in Kappa accuracy. In addition, we select features that can reflect wetlands such as raw band information, spectral index, slope, and elevation, and found that raw band information and spectral index are the most important classification variables by ranking the importance of features.

Citation Download Citation

Xinyue Zhao, Yingdan Wu, Yanxi Lan, Yibei Guo, Yanglin Shao, and Zirui Hu "Application of adaptive weight fusion algorithm to wetland classification in Yellow River Delta", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522R (10 February 2023); https://doi.org/10.1117/12.2667727

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KEYWORDS

Machine learning

Education and training

Remote sensing

Vegetation

Data modeling

Ecosystems

Performance modeling

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