A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm

doi:10.6046/zrzyyg.2022045

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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%.

Keywords Bayesian optimization BO-XGBoost multi-feature cyanobacteria Sentinel-2

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TP79

Issue Date: 20 March 2023

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

Cite this article:

Chen TIAN,Jinlong ZHANG,Yirong JIN, et al. A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm[J]. Remote Sensing for Natural Resources, 2023, 35(1): 49-56.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022045 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/49

Fig.1 Yangcheng Lake water area

Tab.1 Yangcheng Lake image transboundary time

Fig.2 Multi-feature distribution map

Fig.3 Data display on August 26, 2021

Fig.4 Bayesian optimization iteration case

Tab.2 XGBoost optimal hyperparameter combination

Tab.3 Classification Model Accuracy Evaluation

Fig.5 Comparison of cyanobacterial identification results participating in the construction of the sample set in 2021

Fig.6 Comparison of details of cyanobacteria identification results on August 26

Tab.4 Cyanobacteria extraction area and IoU evaluation index

Tab.5 Cyanobacteria extraction area and IoU evaluation index

Fig.7 Comparison of cyanobacterial identification results that did not participate in the construction of the sample set in 2021

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