Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images
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摘要: 利用遥感技术对柴达木盆地枸杞种植区进行精准提取对当地政府开展市场管理与调控具有重要意义。以典型枸杞种植区诺木洪农场为例,选取Landsat8 OLI和GF-1 WFV影像构建作物生长期内时序NDVI/EVI数据,并采用4种新颖的集成学习分类器(LightGBM,GBDT,XGBoost,RF)和2种应用广泛的机器学习分类器(SVM,MLPC)对枸杞种植区进行分类。研究结果表明: ①LightGBM(90.4%),GBDT(90.4%),XGBoost(89.31%)和RF(86.96%)分类器能获得较高的分类精度,并以LightGBM+EVI的总体分类精度最高,达到了91.67%,Kappa系数为0.90; ②EVI指数在枸杞生长中后期表现更为灵敏,并在同一分类器下使用EVI时序数据能获得更好的枸杞作物制图效果; ③利用GBDT,XGBoost和RF分类器的特征重要性评分方法进行枸杞种植区分类时相特征优选,能够在获取高分类精度的同时进一步降低数据冗余。
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关键词:
- 作物分类 /
- 枸杞 /
- NDVI/EVI时序 /
- 集成学习
Abstract: It is significant for the market management and regulation of local government to accurately extract wolfberry planting areas in the Qaidam Basin using remote sensing technology. Taking the Nuomuhong Farm, a typical wolfberry planting area, as an example, this study selected Landsat8 OLI and GF-1 WFV images to construct the time-series NDVI/EVI data of the crop growth period. Then, this study employed four novel ensemble learning classifiers (i.e., LightGBM, GBDT, XGBoost, and RF) and two widely used machine learning classifiers (SVM and MLPC) to classify wolfberry planting areas. The results show that: ① Relatively high classification accuracy were obtained using LightGBM (90.4%), GBDT (90.4%), XGBoost (89.31%), and RF (86.96%). Most especially, LightGBM-EVI yielded the highest overall classification accuracy (91.67%), with a Kappa coefficient of 0.90; ② Enhanced vegetation index (EVI) is more sensitive in the middle-late stage of the wolfberry growth period. For the same classifier, better mapping effects of wolfberry planting areas can be obtained when time-series EVI data were used; ③ Data redundancy can be further reduced while obtaining high classification accuracy by determining the optimal temporal features of NDVI/EVI classification using the feature importance scores of the GBDT, XGBoost, and RF classifiers.-
Key words:
- crop classification /
- wolfberry /
- NDVI/EVI time series /
- ensemble learning
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