基于混合像元分解的HJ-1卫星时间序列影像城市植被信息提取

南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (5): 1058–1067.

基于混合像元分解的HJ-1卫星时间序列影像城市植被信息提取

黄银友1,2,3，冯莉1,2,3，朱榴骏1,2,3，刘寒1,2,3，李成蹊1,2,3

出版日期:2015-09-09 发布日期:2015-09-09
作者简介:(1. 江苏省地理信息技术重点实验室,南京大学,南京,210023; 2.卫星测绘技术与应用国家测绘地理信息局重点实验室,南京大学,南京,210023; 3.南京大学地理信息科学系,南京,210023)
基金资助:
基金项目：国家自然科学基金项目（41301446）

Vegetation information extraction based on HJ-1 satellite time-series images by pixel unmixing

Huang Yinyou1,2,3, Feng Li1,2,3*, Zhu Liujun1,2,3, Liu Han1,2,3,Li Chengxi1,2,3

Online:2015-09-09 Published:2015-09-09
About author:(1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, 210023, China; 2. Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing, 210023, China; 3. Department of Geographic Information Science, Nanjing University, Nanjing, 210023, China)

摘要/Abstract

摘要： 针对城市植被信息提取中单时相遥感影像无法获得物候特征及影像中的混合像元问题，本研究以南京市区及近郊作为研究区，将2013年77景HJ-1 NDVI时间序列数据看成“类高光谱”数据，利用非限定性分解法、半限定性分解法和全限定性分解法对滤波后的数据进行分解，结果表明：使用非限定的线性光谱混合模型分解算法对研究区草地、灌木丛、针叶林、落叶阔叶林、常绿与落叶阔叶混交林5种植被信息提取的RMS误差最小，此方法效果最好。

Abstract: Aiming at the case that single remotely sensed image cannot gain phenological?character and the problem of mixed pixels among the image during urban vegetation information extraction, this paper tried to obtain the distribution information of urban vegetation based on HJ-1 NDVI time-series data, which can be regarded as simulated hyperspectral data. This information can be got by unmixing the S-G filtered NDVI time-series data using unconstrained, semi-constrained and fully constrained linear spectral mixture pixels unmixing algorithm. Taken the downtown and suburbs of Nanjing City as study area，the result indicates that unmixing algorithm of unconstrained linear spectral mixture model can finely extract the five kinds of vegetation information, including shrub, grassland, coniferous forest, broadleaved deciduous forest, evergreen and deciduous broad-leaved mixed forest. The RMS error is minimum among the three methods, and it is considered to be the best

黄银友1,2,3，冯莉1,2,3，朱榴骏1,2,3，刘寒1,2,3，李成蹊1,2,3. 基于混合像元分解的HJ-1卫星时间序列影像城市植被信息提取[J]. 南京大学学报(自然科学版), 2015, 51(5): 1058–1067.

Huang Yinyou1,2,3, Feng Li1,2,3*, Zhu Liujun1,2,3, Liu Han1,2,3,Li Chengxi1,2,3. Vegetation information extraction based on HJ-1 satellite time-series images by pixel unmixing[J]. Journal of Nanjing University(Natural Sciences), 2015, 51(5): 1058–1067.

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