邓继忠 1,2,刘其得 1,2,王长委 3,江秀明 4,朱 圣 1,袁梓文 1,金 鑫 1,朱紫阳 4.基于多时相 Sentinel-2 卫星数据的农作物分类研究[J].广东农业科学,2020,47(4):129-138 |
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基于多时相 Sentinel-2 卫星数据的农作物分类研究 |
Crop Classification Based on Multitemporal Sentinel-2 Satellite Imagery |
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DOI:10.16768/j.issn.1004-874X.2020.04.018 |
中文关键词: Sentinel-2 多时相 植被指数 改进的归一化差异水体指数 颜色指数 农作物分类 |
英文关键词: sentinel-2 multitemporal NDVI MNDWI CI crop classification |
基金项目:国家现代农业产业技术体系棉花产业体系田间管理机械化岗位专家项目(CARS-15-22);广东省级促进经济发展专项资金(GDME-2018E005);地理国情监测国家测绘地理信息局重点实验室开放基金(2018NGCM05);广东省科技计划重大专项(2016B010110005) |
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中文摘要: |
【目的】探究研究区农作物分类最佳时相;结合遥感指数探究一种有效的多时相分类方法,提取主要农作物种植分布情况。【方法】基于多时相 Sentinel-2 卫星数据,采用支持向量机的分类方法对不同时相进行分类,对比分类精度;融合时间序列的 NDVI、MNDWI 指数之后采用支持向量机的方法进行分类,之后分别利用 MNDWI 和 CI 指数结合决策树的分类方法提取水域和田埂。【结果】7 月份的分类效果最好,总体精度达到 91.05%,Kappa 系数达到 0.8518;通过时相数据不同组合的分类精度比较,采用 3—10 月 NDVI 数据叠加后分类的效果较好,总体精度达到 92.25%,Kappa 系数达到 0.8736;对比 3 种不同分类方法,以支持向量机的分类结果精度最高,总体精度达到 94.19%,Kappa 系数达到 0.9024。【结论】7 月份是研究区农作物分类的最佳时相;多时相分类精度明显高于单景数据分类;结合多时相 NDVI、MNDWI、CI 3 种遥感指数进行分类可以有效提取研究区主要农作物的种植分布情况。 |
英文摘要: |
【Objective】The study was to obtain the surface cover and clarify the planting distribution and scope of major crops.【Method】Based on the multitemporal sentinel-2 satellite imagery, 3 remote sensing indexes of NDVI, MNDWI and CI were selected and support vector machine, decision tree auto threshold and random forest were used to classify the single-view data and to evaluate accuracy of every sentinal-2 image with ground field data.【Result】The classification result in July was the best,
with the overall accuracy reaching 91.05% and the Kappa coefficient reaching 0.8518. By comparing the classification accuracy of different combinations of time-phase data, the classification effect was better after overlaying the NDVI data from March to October, with the overall accuracy reaching 92.25% and the Kappa coefficient reaching 0.8736. Compare with the three different classification methods, the classification accuracy of SVM was the highest, with the overall accuracy reaching 94.19% and the Kappa coefficient reaching 0.9024.【Conclusion】July is the best time for crop classification in the study area.The classification accuracy of multi-temporal data is significantly higher than that of single scene data. Classification combined with multi-temporal NDVI, MNDWI and CI can effectively identify the crop planting distribution in the study area. |
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