A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm

TIAN Chen; ZHANG Jinlong; JIN Yirong; DONG Shiyuan; WANG Bin; ZHANG Naixiang

doi:10.6046/zrzyyg.2022045

2023 Vol. 35, No. 1

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TIAN Chen, ZHANG Jinlong, JIN Yirong, DONG Shiyuan, WANG Bin, ZHANG Naixiang. 2023. A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm. Remote Sensing for Natural Resources, 35(1): 49-56. doi: 10.6046/zrzyyg.2022045

Citation:

TIAN Chen, ZHANG Jinlong, JIN Yirong, DONG Shiyuan, WANG Bin, ZHANG Naixiang. 2023. A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm. Remote Sensing for Natural Resources, 35(1): 49-56. doi: 10.6046/zrzyyg.2022045

A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm

1. Suzhou Water Conservancy and Water Information Dispatching Command Center, Suzhou 215011, China；
2. Suzhou Land Think Software Technology Company Limited of Chinese Academy of Science, Suzhou 215163, China

Received Date: 11 February 2022

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

With 14 types of multi-feature information, such as spectrum, index, and texture, of remote sensing images from satellite Sentinel-2 as input and using the Bayesian optimization algorithm, this study designed the BO-XGBoost method used to automatically obtain the optimal hyperparameter combination. This method was successfully applied to the information extraction of cyanobacteria in Yangcheng Lake in 2021. The results show that: ① The optimal hyperparameter combination was obtained using the Bayesian optimization algorithm, and then the BO-XGBoost cyanobacteria classification model was established through obtaining. The training results performed well on the test and training sets, with an accuracy rate of up to 96.07%; ② The BO-XGBoost method was applied to the images used in the sample set. The comparison between the cyanobacteria identification results and the manual interpretation results shows that the two methods yielded roughly the same spatial distribution of cyanobacteria, with a lowest intersection over union (IoU) of 41.31%; ③ To evaluate the applicability of the BO-XGBoost method in other periods, images of other periods were selected for the information extraction of cyanobacteria. As a result, both BO-XGBoost and manual interpretation also yielded roughly the same spatial distribution of cyanobacteria, with a lowest IoU of 43.85%.
- Bayesian optimization /
- BO-XGBoost /
- multi-feature /
- cyanobacteria /
- Sentinel-2

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References

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