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遥感技术与应用  2020, Vol. 35 Issue (3): 558-566    DOI: 10.11873/j.issn.1004-0323.2020.3.0558
LUCC专栏     
时序数据集构建质量对土地覆盖分类精度的影响研究
董超1,2(),赵庚星2()
1.山东农业大学信息科学与技术学院,山东 泰安 271018
2.山东农业大学资源与环境学院,土肥资源高效利用国家工程实验室,山东 泰安 2710181
Influence of Time Series Data Quality on Land Cover Classification Accuracy
Chao Dong1,2(),Gengxing Zhao2()
1.College of Information Science And Engineering, Shandong Agricultural University, Tai'an 271018, China
2.College of Resources and Environment, National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Tai'an 271018, China
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摘要:

研究通过对MODIS双星数据组合、线性插值和HANTS平滑方法来提升时序数据集质量,采用随机森林的方法分类,对分类结果精度评定以分析时序数据集构建质量对分类精度的影响。结果表明:双星数据有利于提高时序数据集的时间分辨率,精确刻划覆盖变化,为后续处理提供基础;线性插值可改善像元点的质量,降低云、雨因素影响;HANTS平滑能移除异常值,平滑数据,突出曲线特征,降低分类复杂度。改进质量后的时序数据集,分类总体精度从84.32%提高至90.75%,Kappa系数从0.798 6提高至0.881 6。总之,使用时序数据进行土地覆盖分类时,应以消除异常值,真实反映地表覆盖物候特征为目的提高时序数据集的质量,从而提高分类精度。

关键词: MODIS时间序列HANTS精度评价随机森林    
Abstract:

This paper improved the quality of time series data sets through three

methods

double star data combination of MODIS, linear interpolation and HANTS smoothing. In this study, we used random forest classification and analyzed the impact of the quality of time series dataset construction on classification accuracy though evaluating the accuracy of classification results. Results showed that the double-star data was beneficial to improve the temporal resolution of time series dataset, accurately depict the coverage change, and provide the basis for subsequent processing; linear interpolation could improve the quality of pixel points and reduce the influence of cloud and rain factors; HANTS smoothing could remove outliers, smooth data, highlight curve features, and reduce classification complexity. After improving the quality of the time series data set, the overall classification accuracy increased from 84.32% to 90.75%, and the Kappa coefficient increased from 79.86% to 88.16%. In a word, when using time series data for land cover classification, the quality of the time series data set should be improved to eliminate the outliers and truly reflect the surface covering phenological features, and the classification accuracy of the results should be improved.

Key words: MODIS    Time series    HANTS    Accuracy assessment    Random forest
收稿日期: 2019-04-15 出版日期: 2020-07-10
ZTFLH:  P237  
基金资助: “十二五”国家科技支撑计划项目(2015BAD23B0202);“双一流”奖补资金(SYL2017XTTD02)
通讯作者: 赵庚星     E-mail: dongchao@sdau.edu.cn;zhaogx@sdau.edu.cn
作者简介: 董超(1984-),男,山东昌邑人,讲师,主要从事农业信息技术研究。E?mail: dongchao@sdau.edu.cn
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引用本文:

董超,赵庚星. 时序数据集构建质量对土地覆盖分类精度的影响研究[J]. 遥感技术与应用, 2020, 35(3): 558-566.

Chao Dong,Gengxing Zhao. Influence of Time Series Data Quality on Land Cover Classification Accuracy. Remote Sensing Technology and Application, 2020, 35(3): 558-566.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.3.0558        http://www.rsta.ac.cn/CN/Y2020/V35/I3/558

数据集名称Terra数据Aqua数据线性插值HANTS
Y23
Y46
S23
S46
P23
P46
PS23
PS46
表1  不同质量时序数据集对照表
图1  研究技术路线图
图2  训练样点分布图
土地利用地类遥感分类地类土地利用地类遥感分类地类
水田

耕地

(一季作物、 两季作物)

坑塘水面水域
水浇地养殖水面
旱地河流水面
果园林草地湖泊水面
有林地水库水面
天然草地城市建设用地
荒草地农村居民点
表2  地类分类对照表
图3  不同处理分类精度
两季作物林草地水域建设用地一季作物
Y23用户精度93.4476.7410083.8268.12
制图精度88.3789.1978.5789.0671.21
Y46用户精度92.6279.0110085.2968.92
制图精度87.6086.4978.5790.6377.27
S23用户精度96.0978.8397.7893.4480.88
制图精度82.1789.1978.5789.0683.33
S46用户精度99.2279.7397.7393.3378.57
制图精度82.0589.8776.7987.0583.33
P23用户精度94.9680.4997.7385.2967.11
制图精度87.6089.1976.7990.6377.27
P46用户精度98.1881.1894.1283.5678.57
制图精度83.7293.2485.7195.3183.33
PS23用户精度93.5580.7210086.3670.83
制图精度89.9290.5478.5789.0677.27
PS46用户精度95.8086.8496.4392.3180.82
制图精度88.3789.1996.4393.7589.39
平均用户精度94.7680.8398.0586.1172.40
制图精度87.6089.6482.4491.4179.29
表3  不同处理时序数据集分类精度
图4  不同时间分辨率曲线对比图
图5  时间节点重要性对比图
图6  线性插值处理对比图
图7  HANTS平滑曲线对比
两季作物一季作物林草地水域建设用地
Y46P46Y46P46Y46P46Y46P46Y46P46
均值5 023.285 022.783 684.123 683.634 841.324 840.8251.9151.992 011.262 010.76
最小值2 666.992 841.891 799.571 848.472 498.692 800.26-1 841.73-1 300.211 011.111 140.41
最大值8 143.528 579.487 881.157 726.977 625.727 308.472 386.401 753.773 059.352 891.24
标准差1 712.041 638.951 840.821 824.661 682.771 657.611 010.20957.94622.89609.75
表4  曲线平滑统计表
Y23Y46S23S46P23P46PS23PS46
训练时间5 s6 s4 s5 s4 s5 s4 s6 s
分类时间36 s37 s36 s37 s34 s35 s33 s34 s
表5  不同处理时序数据集分类时间
图8  分类结果图
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