Wetland classification in the Guangdong-Hong Kong-Macao Greater Bay Area based on GEE and aux-coatnet

Anjun Lou; Zhi He; Guanglin Lai; Liwei Zou; Man Xiao; Chengle Zhou

doi:10.1117/12.3011112

15 November 2023 Wetland classification in the Guangdong-Hong Kong-Macao Greater Bay Area based on GEE and aux-coatnet

Anjun Lou, Zhi He, Guanglin Lai, Liwei Zou, Man Xiao, Chengle Zhou

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281521 (2023) https://doi.org/10.1117/12.3011112
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Classification of wetlands over large area is crucial for monitoring wetland ecological resources. In this paper, the Google Earth Engine (GEE) platform was integrated with deep learning technology to enhance the classification accuracy of wetlands in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Firstly, the GEE was utilized for preprocessing the 2022 Landsat images of the GBA to determine the maximum wetland extent. Subsequently, the the Coatnet model with Auxiliary classifier(Aux-Coatnet) was employed to classify the annual wetland image. This model demonstrated an overall accuracy(OA) improvement between 6.13% and 24.52%, outperforming other models with balanced performance across diverse land use types. Particularly, it proved proficient in extracting wetland features like rivers, lakes or reservoirs, mangrove or swamp wetlands, and aquaculture pond. These findings highlight the potential of the Aux-Coatnet model in achieving high-accuracy extraction and classification of large-scale wetland features

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Anjun Lou, Zhi He, Guanglin Lai, Liwei Zou, Man Xiao, and Chengle Zhou "Wetland classification in the Guangdong-Hong Kong-Macao Greater Bay Area based on GEE and aux-coatnet", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281521 (15 November 2023); https://doi.org/10.1117/12.3011112

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KEYWORDS

Image classification

Deep learning

Landsat

Performance modeling

Random forests

Convolution

Transformers

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