A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm
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摘要: 利用Sentinel-2遥感卫星影像,结合遥感优势以光谱、指数、纹理等14种多种特征信息为输入,依托贝叶斯优化算法,设计了一种能自动获取最优超参数组合的BO-XGBoost方法,并将其成功应用于2021年阳澄湖蓝藻信息提取。结果表明: ①通过贝叶斯优化算法获取最优超参数组合,进行训练得到BO-XGBoost蓝藻分类模型,其训练结果在测试集和训练集上表现效果良好,准确率高达96.07%; ②将BO-XGBoost应用于参与样本集构建的影像,其蓝藻识别结果与人工解译成果对比,2种方法得到的蓝藻空间分布情况基本一致,交并比最低为41.31%; ③为评价该分类模型在其他时相的适用性,选择其他时相影像数据进行蓝藻提取,BO-XGBoost与人工解译2种方法蓝藻空间分布情况基本一致,交并比最低为43.85%。
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关键词:
- 贝叶斯优化 /
- BO-XGBoost /
- 多特征 /
- 蓝藻 /
- Sentinel-2
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%.-
Key words:
- Bayesian optimization /
- BO-XGBoost /
- multi-feature /
- cyanobacteria /
- Sentinel-2
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[1] 胡智华, 林妙丽, 李港, 等. 城市闸控河流浮游植物群落结构特征及影响因素[J]. 环境科学学报, 2021, 41(9):3631-3640.
[2] Hu Z H, Lin M L, Li G, et al. Phytoplankton community characteristics and influencing factors of the urban gate-controlled rivers[J]. Acta Scientiae Circumstantiae, 2021, 41(9):3631-3640.
[3] 岩腊, 龙笛, 白亮亮, 等. 基于多源信息的水资源立体监测研究综述[J]. 遥感学报, 2020, 24(7):787-803.
[4] Yan L, Long D, Bai L L, et al. A review on water resources stereoscopic monitoring systems based on multisource data[J]. National Remote Sensing Bulletin, 2020, 24(7):787-803.
[5] 谢勇, 李凯云, 李家国, 等. Sentinel-2A影像珠海市污染水体识别[J]. 遥感信息, 2021, 36(3):1-9.
[6] Xie Y, Li K Y, Li J G, et al. Water pollution identification in Zhuhai City based on Sentinel-2A imagery[J]. Remote Sensing Information, 2021, 36(3):1-9.
[7] 邱亚会, 卢剑波. 浒苔遥感监测研究进展[J]. 生态学报, 2015, 35(15):4977-4985.
[8] Qiu Y H, Lu J B. Advances in the monitoring of Enteromorpha prolifera using remote sensing[J]. Acta Ecologica Sinica, 2015, 35(15):4977-4985.
[9] Xing Q G, Hu C M. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data:Application of a virtual baseline reflectance height technique[J]. Remote Sensing of Environment, 2016, 178:113-126.
[10] 陈春波, 周宝同, 田永中, 等. 环境一号卫星多光谱数据在巢湖动态监测中的应用[J]. 中国环境监测, 2014, 30(1):200-204.
[11] Chen C B, Zhou B T, Tian Y Z, et al. Application of environmental satellite HJ1A/1B-CCD data for Cyanobacteria dynamic monitoring in Chaohu Lake[J]. Environmental Monitoring of China, 2014, 30(1):200-204.
[12] Hu C. A novel ocean color index to detect floating algae in the global oceans[J]. Remote Sensing of Environment, 2009, 113(10):2118-2129.
[13] Fang C, Song K, Shang Y, et al. Remote sensing of harmful algal blooms variability for Lake Hulun using adjusted FAI (AFAI) Algorithm[J]. Journal of Environmental Informatics, 2018:54-68.
[14] Otukei J R, Blaschke T. Land cover change assessment using decision trees,support vector machines and maximum likelihood classification algorithms[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12:27-31.
[15] 李旭青, 刘世盟, 李龙, 等. 基于RF算法优选多时相特征的冬小麦空间分布自动解译[J]. 农业机械学报, 2019, 50(6):218-225.
[16] Li X Q, Liu S M, Li L, et al. Automatic interpretation of spatial distribution of winter wheat based on random forest algorithm to optimize multi-temporal features[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(6):218-225.
[17] 李苓苓, 潘耀忠, 张锦水, 等. 支持向量机与分类后验概率空间变化向量分析法相结合的冬小麦种植面积测量方法[J]. 农业工程学报, 2010, 26(9):210-217.
[18] Li L L, Pan Y Z, Zhang J S, et al. Method of winter wheat planting area estimation based on support vector machine and post-classification changed vector analysis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(9):210-217.
[19] 赵莲, 张锦水, 胡潭高, 等. 变端元混合像元分解冬小麦种植面积测量方法[J]. 国土资源遥感, 2011, 23(1):66-72.doi:10.6046/gtzyyg.2011.01.13.
[20] Zhao L, Zhang J S, Hu T G, et al. The application of the dynamic endmember linear spectral unmixing model to winter wheat area estimation[J]. Remote Sensing for Land and Resources, 2011, 23(1):66-72.doi:10.6046/gtzyyg.2011.01.13.
[21] 王怡人, 王胜强, 喻樾, 等. 一种提取南黄海浒苔的自适应阈值遥感算法[J]. 遥感信息, 2021, 36(2):120-129.
[22] Wang Y R, Wang S Q, Yu Y, et al. An adaptive threshold algorithm for detecting ulva prolifera in southern Yellow Sea by remote sensing[J]. Remote Sensing Information, 2021, 36(2):120-129.
[23] Chen T W, Guestrin C. XGBoost:A scalable tree boosting system[C]// 22nd ACM SIGKDD International Conference.ACM, 2016.
[24] 卢雪梅, 苏华. 基于OLCI数据的福建近海悬浮物浓度遥感反演[J]. 环境科学学报, 2020, 40(8):2819-2827.
[25] Lu X M, Su H. Retrieving total suspended matter concentration in Fujian coastal waters using OLCI data[J]. Acta Scientiae Circumstantiae, 2020, 40(8):2819-2827.
[26] 黄宇, 潘励. 基于显著性图像与纹理特征的遥感影像云检测[J]. 测绘地理信息, 2021, 46(2):16-19.
[27] Huang Y, Pan L. Cloud detection of remote sensing images based on significance maps and texture features[J]. Journal of Geomatics, 2021, 46(2):16-19.
[28] 徐逸, 甄佳宁, 蒋侠朋, 等. 无人机遥感与XGBoost的红树林物种分类[J]. 遥感学报, 2021, 25(3):737-752.
[29] Xu Y, Zhen J N, Jiang X P, et al. Mangrove species classification with UAV-based remote sensing data and XGBoost[J]. National Remote Sensing Bulletin, 2021, 25(3):737-752.
[30] 李鹏, 冯存前, 许旭光, 等. 一种利用贝叶斯优化的弹道目标微动分类网络[J]. 西安电子科技大学学报, 2021, 48(5):139-148.
[31] Li P, Feng C Q, Xu X G, et al. Ballistic target fretting classification network based on Bayesian optimization[J]. Journal of Xidian University, 2021, 48(5):139-148.
[32] 孙淑文, 夏霆, 杨金艳, 等. 苏州湖泊水源地浮游植物群落特征及营养状况分析[J]. 中国农村水利水电, 2020(8):51-55.
[33] Sun S W, Xia T, Yang J Y, et al. An analysis of the phytoplankton community characteristics and nutrition status in lake-type drinking water sources in Suzhou[J]. China Rural Water and Hydropower, 2020(8):51-55.
[34] 刘瑞清, 李加林, 孙超, 等. 基于Sentinel-2遥感时间序列植被物候特征的盐城滨海湿地植被分类[J]. 地理学报, 2021, 76(7):1680-1692.
[35] Liu R Q, Li J L, Sun C, et al. Classification of Yancheng coastal wetland vegetation based on vegetation phenological characteristics derived from Sentinel-2 time-series[J]. Acta Geographica Sinica, 2021, 76(7):1680-1692.
[36] Guo X J, Zhang C C, Luo W R, et al. Urban impervious surface extraction based on multi-features and random forest[J]. IEEE Access, 2020, 8:226609-226623.
[37] Bergstra J, Bardenet R, Bengio Y, et al. Algorithms for hyper-parameter optimization[J]. Advances in Neural Information Processing Systems, 2011, 24(24):2546-2554.
[38] Tanaka R, Iwata H. Bayesian optimization for genomic selection:A method for discovering the best genotype among a large number of candidates[J]. Theoretical and Applied Genetics, 2017, 131(1):1-13.
[39] Seeger M. Gaussian processes for machine learning[J]. International Journal of Neural Systems, 2004, 14(2):69-106.
[40] Colopy G W, Roberts S J, Clifton D A. Bayesian optimization of personalized models for patient vital-sign monitoring[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(2):301.
[41] 陈云, 戴锦芳. 基于遥感数据的太湖蓝藻水华信息识别方法[J]. 湖泊科学, 2008, 20(2):179-183.
[42] Chen Y, Dai J F. Extraction methods of cyanobacteria bloom in Lake Taihu based on RS data[J]. Journal of Lake Science, 2008, 20(2):179-183.
[43] 张东彦, 尹勋, 佘宝, 等. 多源卫星遥感数据监测巢湖蓝藻水华爆发研究[J]. 红外与激光工程, 2019, 48(7):303-314.
[44] Zhang D Y, Yin X, She B, et al. Using multi-source satellite imagery data to monitor cyanobacterial blooms of Chaohu Lake[J]. Infrared and Laser Engineering, 2019, 48(7):303-314.
[45] 刘海秋, 任恒奎, 牛鑫鑫, 等. 基于Sentinel-2遥感影像的巢湖蓝藻水华提取方法研究[J]. 生态环境学报, 2021, 30(1):146-155.
[46] Liu H Q, Ren H K, Niu X X, et al. Extraction of cyanobacteria bloom in Chaohu Lake based on Sentinel-2 remote sensing images[J]. Ecology and Environmental Sciences, 2021, 30(1):146-155.
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