最小二乘混合像元分解的端元丰度信息提取研究Research on the extraction of surface feature abundance based on the least square mixed pixel decomposition
杨超,王金亮,渠立权,孙兴齐,李石华
摘要(Abstract):
针对无约束最小二乘混合像元分解算法提取地物端元丰度出现的局限性问题,通过野外实地采集的地物光谱数据建立研究区典型的地物波谱库,以Landsat OLI影像作为主要数据源,在经过Gram-Schmidt(GS)影像融合的基础上,利用纯净像元指数(PPI)及基于几何顶点的端元提取技术提取研究区典型地物端元,最后通过完全约束的最小二乘混合像元分解算法完成对研究区典型地物端元丰度的提取。结果较好地解决了无约束最小二乘混合像元分解算法提取的端元丰度信息出现负值的情况,并且提高了典型地物丰度信息提取的精度。完全约束最小二乘混合像元分解算法的RMSE误差均控制在0.174 913左右,在很大程度上提高了混合像元分解精度及实用性。
关键词(KeyWords): Gram-Schmidt影像融合;PPI;最小二乘算法;端元;混合像元分解
基金项目(Foundation): 国家自然科学基金项目(41561048);; 云南省中青年学术技术带头人培养项目(2008PY056);; 国家测绘地理信息局地理国情监测示范项目“抚仙湖流域生态环境动态监测”测国土函[2014]35号
作者(Author): 杨超,王金亮,渠立权,孙兴齐,李石华
DOI: 10.16251/j.cnki.1009-2307.2017.09.026
参考文献(References):
- [1]张采芳,田岩,张荟平.基于类半径不确定性度量的遥感影像分类[J].遥感信息,2015,30(3):111-115.(ZHANG Caifang,TIAN Yan,ZHANG Huiping.Remote sensing classification base on uncertainty measure of radius of category[J].Remote Sensing Information,2015,30(3):111-115.)
- [2]李剑萍,郑有飞.气象卫星混合像元分解研究综述[J].中国农业气象,2000,21(2):44-45.(LI Jianping,ZHENG Youfei.Study on the mixed pixels decomposition methods of meteorological satellite[J].Chinese Journal of Agrometeorology,2000,21(2):44-45.)
- [3]刘文国,唐芳林,刘绍娟.西南大旱对不同森林植被流域的影响:以松华坝水库和云龙水库为例[J].林业经济,2012(10):12-17.(LIU Wenguo,TANG Fanglin,Liu Shaojuan.The southwest drought on different forest vegetation watershed:taking songhuaba reservoir and Yunlong reservoir as an example[J].Forestry Economics,2012(10):12-17.)
- [4]JENSEN J R.Introductory digital image processing:a remote sensing perspective[M].3rd Edition.New Jersey:PrenticeHall,2004.
- [5]郝兰朋.面向对象技术在城市建筑物提取中的应用研究[D].西安:西安科技大学,2011.(HAO Lanpeng.Applied research of extracting buildings information based on object-oriented[D].Xi'an:Xi'an University of Science and Technology,2011:20-39.)
- [6]LI C J,LIU L Y,WANG J H,et al.Comparison of twomethods of fusing remote sensing imageswith fidelity of spectra information[J].Journalof Image and Graphics,2004,9(11):1376-1385.
- [7]刘勇,岳文泽.基于图像融合与混合像元分解的城市植被盖度提取[J].生态学报,2010,31(3):93-99.(LIU Yong,YUE Wenze.Estimation of urban vegetation fraction by image fusion and spectral unmixing[J].Acta Ecologica Sinica,2010,31(3):93-99.)
- [8]ROBERTS D A,GARDNER M,CHURCH R,et al.Mapping chaparral in the Santa Monica Mountains using multiple endmemberspectral mixture model[J].Remote Sensing of Environment,1998,65(3):267-279.
- [9]ROBERTS D A,BATISTA G T,PEREIRA J L G,et al.Change identification using multi-temporal spectral mixture analysis.[C]//Applications in Eastern Amazonia.Remote Sensing ChangeDetection:Environmental Monitoring Applications and Methods.Michigan:Ann Arbor Press,1999:137-161.
- [10]RASHED T,WEEKS J R,GADALLA M S,et al.Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery:a case study of the greater cairo region,egypt[J].Geocarto International,2001,16(4):5-16.
- [11]RASHED T,WEEKS J R,ROBERTS D,et al.Measuring the physical composition of urban morphology using multiple end-member spectral mixture models[J].Photogrammetric Engineering&Remote Sensing,2003,69(9):1011-1020.
- [12]SMITH M O,USTIN S L,ADAMS J B,et al.Vegetation indeserts[J].Remote Sensing of Environment,1990,31(1):1-26.
- [13]ROBERTS D A,SMITH M O,ADAMS J B.Green vegetation,non-photosynthetic vegetation,and soil in AVIRIS[J].Remote Sensing of Environment,1993,44(2-3):255-269.
- [14]徐君,宋凯,李波,等.数据约简化的高光谱影像端元提取[J].红外技术,2016,38(6):481-485.(XUE Jun,SONG Kai,LI Bo,et al.Endmember extraction algorithm base on hyperspectral data simplification[J].Infrared Technology,2016,38(6):481-485.)
- [15]高晓惠,相里斌,魏儒义.基于光谱分类的端元提取研究[J].光谱学与光谱分析,2011,31(7):1995-1998.(GAO Xiaohui,XIANG Libin,WEI Ruyi.Based spectral classification of endmember extraction algorithm[J].Spectroscopy and Spectral Analysis,2011,31(7):1995-1998.)
- [16]CHANG C I,Plaza A.A fast iterative algorithm for implementation of pixel purity index[J].Geoscience and Remote Sensing Letters,IEEE,2006,3(1):63-67.
- [17]GRUNINGER J H,RATKOWSKI A J,HOKE M L.The sequential maximum angle convex cone(SMACC)endmember model[J].Proc Spie,2004,5425:1-14.
- [18]李素,李文正,周建军,等.遥感影像混合像元分解中的端元选择方法综述[J].地理与地理信息科学,2007,23(5):35-42.(LI Su,LI Wenzheng,ZHOU Jianjun,et al.Mixed pixel decomposition of remote sensing images endmember selection review[J].Geography and GeoInformation Science,2007,23(5):35-42.)
- [19]LU D S,BATISTELLA M,MORAN E.Multi-temporal spectralmixture analysis for Amazonian land-cover change detection[J].Canada Journal of Remote Sensing,2004,30(1):87-100.
- [20]TOMPKINS S,MUSTARD J F,PIETERS C M.Optimizationof endmembers for spectral mixture analysis[J].Remote Sensing of Environment,1997,59:472-489.
- [21]许宁,胡玉新,雷斌,等.一种基于PPI的高光谱数据矿物信息自动提取方法[J].测绘科学,2013,38(4):138-141.(XU Ning,HU Yuxin,LEI Bin,et al.Automated mineral information extraction based on PPI algorithm for hyperspectral imagery[J].Science of Surveying and Mapping,2013,38(4):138-141.)
- [22]朱怀朝.带约束的最小二乘求解算法及其在高光谱遥感混合像元中的应用[D].成都:成都理工大学,2012:7-45.(Zhu Huaizhao.The application of unmixed pixels with constrained least squares algorithm in hyperspectral remote sensing[D].chengdu:Chengdu University of Technology,2012:7-45.)
- [23]张飞飞,孙旭,薛良勇,等.融合简单线性迭代聚类的高光谱混合像元分解策略[J].农业工程学报,2015,31(17):199-206.(ZHANG Feifei,SUN Xu,XUE Liangyong,et al.Hyperspectral mixed pixel decomposition policy merging simple linear iterative clustering[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(17):199-206.)