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国土资源遥感  2020, Vol. 32 Issue (1): 19-26    DOI: 10.6046/gtzyyg.2020.01.04
     技术方法 本期目录 | 过刊浏览 | 高级检索 |
时间序列低分影像修正中分遥感冬小麦分布
朱爽1,2,张锦水2,3,4()
1. 北京工业职业技术学院,北京 100042
2. 北京师范大学地表过程与资源生态国家重点实验室,北京 100875
3. 北京市陆表遥感数据产品工程技术研究中心,北京 100875
4. 北京师范大学地理科学学部遥感科学与工程研究院,北京 100875
Medium resolution remote sensing based winter wheat mapping corrected by low-resolution time series remote sensing images
Shuang ZHU1,2,Jinshui ZHANG2,3,4()
1. Beijing Polytechnic College, Beijing 100042, China
2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3. Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
4. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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摘要 

单期中等空间分辨率遥感影像(如Landsat8 OLI)进行冬小麦提取,易受到“异物同谱、同物异谱”影响,造成冬小麦识别结果的“错入、错出”,降低冬小麦识别精度。低空间分辨率遥感影像(如MODIS)获取时间频率高,具有时间序列特征,能够准确地刻画出冬小麦生长周期内的特有物候特征,可以有效地消除单期遥感影像上存在的“异物同谱、同物异谱”现象。研究利用MODIS时间序列特征提取出的冬小麦空间分布信息为辅助信息,用来修正单期OLI遥感影像识别冬小麦结果的“错入、错出”误差,以提高冬小麦的识别精度。实验结果表明,在冬小麦错出区域,OLI提取结果的均方根误差(root mean square error,RMSE)为0.758,经MODIS修正后RMSE为0.142,降低了0.616; 在冬小麦错入区域,OLI提取结果的RMSE为0.901,经MODIS修正后RMSE为0.122,降低了0.779。可见,该方法能够发挥MODIS有效描述冬小麦生长周期内时间序列特征的优势,对Landsat OLI冬小麦测量结果进行了有效修正,提高了冬小麦测量精度。

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朱爽
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关键词 时间序列线性混合像元分解丰度一致性分析修正    
Abstract

Owing to influences of the same spectrum with the different thing and the same thing with the different spectrum, the medium resolution remotely sensed image, Landsat8 OLI, extracts the wheat extraction with the wrong information, which leads to low accuracy. The coarse resolution image with multi-temporal trait can discriminate the wheat information from other similar land cover. In this paper, the multi-temporal trait is adopted to solve the “wrong coming or wrong going” error of the OLI classification so as to increase the wheat extraction accuracy. The experiment shows that the OLI and MODIS can extract the wheat with high consistence, so the result of MODIS can correct the error of the OLI, where the phenomenon of the same spectrum with the different thing and the same thing with the different spectrum occurs. In the region of the same thing with different spectrum, the RMSE of OLI result is 0.758, while that of the MODIS correction result is 0.142. In the region of the different thing with the same spectrum, the RMSE of OLI result is 0.901, while that of the MODIS correction result is 0.122. All the results show that the MODIS result can correct OLI result for higher wheat extraction accuracy, which can solve the phenomenon of the same spectrum with the different thing and the same thing with the different spectrum.

Key wordstime series    linear mixed spectral unmixing    fraction    consistency analysis    rectification
收稿日期: 2019-02-15      出版日期: 2020-03-14
ZTFLH:  TP79  
基金资助:高分辨率对地观测系统重大专项支持项目民用部分(编号: 09-Y20A05-9001-17/18)
通讯作者: 张锦水     E-mail: zhangjs@bnu.edu.cn
作者简介: 朱 爽(1981-),女,博士,副教授,主要研究方向为环境遥感。Email: zhushuang@mail.bnu.edu.cn。
引用本文:   
朱爽,张锦水. 时间序列低分影像修正中分遥感冬小麦分布[J]. 国土资源遥感, 2020, 32(1): 19-26.
Shuang ZHU,Jinshui ZHANG. Medium resolution remote sensing based winter wheat mapping corrected by low-resolution time series remote sensing images. Remote Sensing for Land & Resources, 2020, 32(1): 19-26.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.01.04      或      http://www.gtzyyg.com/CN/Y2020/V32/I1/19
Fig.1  研究区OLI B4(R),B5(G),B3(B)假彩色合成影像
Fig.2  OLI影像上典型地物光谱曲线
Fig.3  技术路线
Fig.4  研究区OLI影像冬小麦提取结果
类别 野外测量数据 生产者
精度/%
冬小麦 非小麦
OLI分类数据 冬小麦 237 61 79.53
非冬小麦 43 555 7.19
用户精度/% 84.64 9.90
Tab.1  OLI影像冬小麦识别精度
Fig.5  典型地物的时间序列曲线
Fig.6  MODIS端元散点图及提取的冬小麦分布
Fig.7  MODIS-OLI冬小麦测量一致性分析
Fig.8  MODIS修正OLI冬小麦错出结果
Fig.9  MODIS修正OLI冬小麦错入结果
区域 MODIS丰度范围/% MODIS平均丰度/% OLI丰度范围/% OLI平均丰度/% RMSE(OLI) RMSE(MODIS修正)
冬小麦错出 70.2~100 84.9 0~20 8.6 0.758 0.142
冬小麦错入 0~35 16.3 60~90 78.6 0.901 0.122
Tab.2  OLI冬小麦错出和错入区域MODIS冬小麦测量结果误差分析
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