Google Earth Engine支持下的江苏省夏收作物遥感提取
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摘要
江苏省是农作物种植大省,国家统计局统计数据显示,江苏省近10年冬小麦、冬油菜的总播种面积分列全国第五、第七,快速准确地获取冬小麦和冬油菜的空间分布对于该省的农业发展具有重意义。基于单机的传统遥感分类能够准确获取农作物的空间分布信息,但是耗时较长。随着地理大数据与云平台、云计算的发展,Google Earth Engine(GEE)作为一个基于云平台的全球尺度地理空间分析平台,为快速遥感分类带来了新的机遇。本文基于GEE,使用Sentinel-2数据快速提取了江苏省2017年冬小麦与冬油菜的空间分布。首先,利用GEE获得覆盖江苏省119景无云质优的Sentinel-2影像;其次,在此基础上分别计算了遥感指数、纹理特征、地形特征,并完成原始特征的构建与优化;最后,分别试验了朴素贝叶斯、支持向量机、分类回归树和随机森林4种分类器,比较了各分类器的分类精度,并提取了冬小麦与冬油菜的空间分布信息。得出以下结论:①GEE能够快速完成覆盖江苏省影像数据的去云、镶嵌、裁剪及特征构建等预处理,较本地处理具有明显优势;②J-M距离值位于前两位且大于1将特征数量从28个压缩到11个,有效压缩了原始特征空间;③光谱+纹理+地形特征组合训练,朴素贝叶斯、支持向量机、分类回归树、随机森林的平均验证精度分别为61%、87%、89%、92%。
Jiangsu province, with 13 municipalities and located in the east of China, is an important part of the Yangtze river delta economy belt. The temperature is appropriate and the rainfall is moderate. Jiangsu province enjoys a moderate climate, which is suitable for the agricultural development. Winter wheat is distributed throughout the whole province, whereas the planting structure of winter rapeseed is complex and mainly scattered in Southern Jiangsu. As reported by the State Statistics Bureau, the total planting area of winter wheat and winter rapeseed in Jiangsu ranked the fifth and seventh in China, respectively, during the last 10 years. Fast obtaining the precise planting area of these two crops in Jiangsu is crucial for the agricultural development.Remote sensing classification based on local host can obtain spatial distribution of crops with high accuracy, but is time-consuming. With the development of geographical big data, cloud platform, and cloud computation, the Google Earth Engine(GEE), a global scale geospatial analysis platform based on the cloud platform, has brought new opportunities for rapid remote sensing classification. Based on the GEE cloud platform, a time-saving method of obtaining the spatial distribution of winter wheat and winter rapeseed by use of sentinel-2 data in Jiangsu was proposed. First, 119 sentinel-2 images without cloud were obtained using the GEE in Jiangsu. The time interval was set from March 1 to June 1, 2017, and the space area was Jiangsu province. Based on the spatio-temporal information, the 119 remote sensing images were mosaicked and clipped. Secondly, remote sensing indices, texture, and terrain features were calculated respectively, and the original features were extracted. The original feature space was optimized by an algorithm named Separability and Thresholds(SEaTH algorithm). Finally, four classifiers including naive Bayes, support vector machine, classification regression tree,and random forest were tested and evaluated by the average assessment accuracy. The spatial distribution information of winter wheat and winter rapeseed were obtained quickly. The following conclusions are drawn:(1) the GEE can quickly complete pre-processing of cloud-masking, image-mosaicking, image-clipping, and feature extraction, which is superior to the local processing.(2) The distance values of J-M that are higher than 1 and rank top two highest can reduce the number of features from 28 to 11 and effectively compress the original feature space.(3) With the combined training of spectral, texture and terrain features, the average assessment accuracy of naive Bayes, support vector machine, classification regression tree, and random forest was 61%,87%, 89% and 92%, respectively.
Jiangsu province, with 13 municipalities and located in the east of China, is an important part of the Yangtze river delta economy belt. The temperature is appropriate and the rainfall is moderate. Jiangsu province enjoys a moderate climate, which is suitable for the agricultural development. Winter wheat is distributed throughout the whole province, whereas the planting structure of winter rapeseed is complex and mainly scattered in Southern Jiangsu. As reported by the State Statistics Bureau, the total planting area of winter wheat and winter rapeseed in Jiangsu ranked the fifth and seventh in China, respectively, during the last 10 years. Fast obtaining the precise planting area of these two crops in Jiangsu is crucial for the agricultural development.Remote sensing classification based on local host can obtain spatial distribution of crops with high accuracy, but is time-consuming. With the development of geographical big data, cloud platform, and cloud computation, the Google Earth Engine(GEE), a global scale geospatial analysis platform based on the cloud platform, has brought new opportunities for rapid remote sensing classification. Based on the GEE cloud platform, a time-saving method of obtaining the spatial distribution of winter wheat and winter rapeseed by use of sentinel-2 data in Jiangsu was proposed. First, 119 sentinel-2 images without cloud were obtained using the GEE in Jiangsu. The time interval was set from March 1 to June 1, 2017, and the space area was Jiangsu province. Based on the spatio-temporal information, the 119 remote sensing images were mosaicked and clipped. Secondly, remote sensing indices, texture, and terrain features were calculated respectively, and the original features were extracted. The original feature space was optimized by an algorithm named Separability and Thresholds(SEaTH algorithm). Finally, four classifiers including naive Bayes, support vector machine, classification regression tree,and random forest were tested and evaluated by the average assessment accuracy. The spatial distribution information of winter wheat and winter rapeseed were obtained quickly. The following conclusions are drawn:(1) the GEE can quickly complete pre-processing of cloud-masking, image-mosaicking, image-clipping, and feature extraction, which is superior to the local processing.(2) The distance values of J-M that are higher than 1 and rank top two highest can reduce the number of features from 28 to 11 and effectively compress the original feature space.(3) With the combined training of spectral, texture and terrain features, the average assessment accuracy of naive Bayes, support vector machine, classification regression tree, and random forest was 61%,87%, 89% and 92%, respectively.
引文
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[4]班松涛.县域农作物分类类型遥感识别与提取[D].西安:西北农林科技大学,2014.[Ban S T. Remote sensing identification and extraction of county crop classification types[D]. Xi'an:Northwest A&F University, 2014.]
[5]程彬.基于支持向量机的乾安县土地利用遥感分类研究[J].长春师范大学学报,2017,36(12):86-88.[Cheng B.Land use information extraction based on support vector machine using multitemporal remote sensing in Qian'an county[J]. Journal of Changchun Normal University,2017,36(12):86-88.]
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[7]张静,张翔,田龙,等.西北旱区遥感影像分类的支持向量机法[J].测绘科学,2017,42(1):49-52.[Zhang J, Zhang X,Tian L, et al. The support vector machine method for RS images'classification in northwest arid area[J]. Science of Surveying and Mapping, 2017,42(1):49-52.]
[8]杨屹鹍,蒋平安,武红旗,等.基于高分一号与Landsat 8卫星影像的库尔勒市香梨种植面积识别研究[J].山东农业科学,2018,50(1):153-157.[Yang Y K, Jiang P A, Wu H Q,et al. Recognition research of Korla fragrant pear planting area based on satellite images of GF-1 and Landsat 8[J].Shandong Agricultural Sciences, 2018,50(1):153-157.]
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[10] Dong J, Xiao X, Menarguez MA, et al. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and google earth engine[J]. Remote Sensing of Environment, 2016,185:142-154.
[11]徐晗泽宇,刘冲,王军邦,等. Google Earth Engine平台支持下的赣南柑橘果园遥感提取研究[J].地球信息科学学报, 2018,20(3):396-404.[Xu H Z Y, Liu C, Wang J B, et al. Study on extraction of citrus orchard in Gannan region based on google earth engine platform[J]. Journal of GeoInformation Science, 2018,20(3):396-404.]
[12] Shelestov A, Lavreniuk M, Kussul N, et al. Exploring google earth engine platform for big data processing:classification of multi-temporal satellite imagery for cop mapping[J]. Frontiers in Earth Science, 2017,5(5):1-10.
[13]胡云锋,商令杰,王召海,等.GEE平台和CART方法的北京市土地解译[J].测绘科学,2018,43(4):87-93.[Hu Y F,Shang L J, Wang Z H, et al. Land-cover and land-use classification in Beijing based on cart and gee[J]. Science of Surveying and Mapping, 2018,43(4):87-93.]
[14] Xiong J, Thenkabail P S, Gumma M K, et al. Automated cropland mapping of continental Africa using google earth engine cloud computing[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2017,126:225-244.
[15]王松林.基于多源遥感的江苏省油菜及其同物候期主要作物面积提取[D].武汉:长江大学,2015.[Wang S L. Extraction of main crop areas in rapeseed and its phenological period in Jiangsu province based on multi-source remote sensing[D]. Wuhan:Yangtze University, 2015.]
[16] Farr T G, Rosen P A, Caro E, et al. The shuttle radar topography mission[J]. Reviews of Geophysics, 2007,45(2):361.
[17]贾坤,李强子.农作物遥感分类特征变量选择研究现状与展望[J].资源科学,2013,35(12):2507-2516.[Jia K, Li Q Z. Review of features selection in crop classification using remote sensing data[J]. Resources Science, 2013,35(12):2507-2516.]
[18] Yang X, Zhao S, Qin X, et al. Mapping of urban surface water bodies from Sentinel-2 msi imagery at 10 m resolution via ndwi-based image sharpening[J]. Remote Sensing, 2017,9(6):596-614.
[19] Li K, Chen Y. A genetic algorithm-based urban cluster automatic threshold method by combining viirs dnb, ndvi,and ndbi to monitor urbanization[J]. Remote Sensing,2018,10(2):277-297.
[20]裴欢,孙天娇,王晓妍.基于Landsat 8 OLI影像纹理特征的面向对象土地利用/覆盖分类[J].农业工程学报,2018,34(2):248-255.[Pei H, Sun T J, Wang X Y. Object-oriented land use/cover classification based on texture features of Landsat 8 OLI image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(2):248-255.]
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