A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm
TIAN Chen1(), ZHANG Jinlong1, JIN Yirong1, DONG Shiyuan2(), WANG Bin2, ZHANG Naixiang2
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
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%.
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2023, Vol. 35 
), 张金龙1, 金义蓉1, 董世元2(
