多端元光谱解混模型的改进及对植被盖度的反演
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摘要
以扎龙自然保护区为研究对象,运用分层的面向对象分类法与多端元光谱解混算法相结合反演该地区的植被覆盖度。结果表明:分层降低了场景复杂度,面向对象分类法与多端元光谱解混算法的结合,有效的减少了计算量和混合像元的端元变化;采用同期高分辨率的SPOT5多光谱遥感影像进行精度验证,与传统的多端元光谱解混模型的反演结果进行对比,相关系数从0.864 3提高到0.902 8,均方根误差从0.171 2减少到0.092 6。因此,分层面向对象多端元光谱解混模型适合对湿地植被覆盖度的反演。
In Zhalong Nature Reserve,the vegetation coverage of the area was retrieved by combining hierarchical object-oriented classification with Multiple Endmember Spectral Mixture Analysis( MESMA). Stratification reduced the complexity of the scene,the combination of object-oriented classification and MESMA effectively reduced computation and endmember variation of mixed pixels. A high resolution SPOT5 multi spectral remote sensing image was used to verify the accuracy of the same time. Compared with the inversion results of the traditional MESMA,the correlation coefficient was increased from 0.864 3 to 0.902 8,and the root mean square error was decreased from 0.171 2 to 0.092 6. Therefore,the hierarchical objectoriented Multiple Endmember Spectral Mixture Model is suitable for the inversion of the vegetation coverage of the wetland.
In Zhalong Nature Reserve,the vegetation coverage of the area was retrieved by combining hierarchical object-oriented classification with Multiple Endmember Spectral Mixture Analysis( MESMA). Stratification reduced the complexity of the scene,the combination of object-oriented classification and MESMA effectively reduced computation and endmember variation of mixed pixels. A high resolution SPOT5 multi spectral remote sensing image was used to verify the accuracy of the same time. Compared with the inversion results of the traditional MESMA,the correlation coefficient was increased from 0.864 3 to 0.902 8,and the root mean square error was decreased from 0.171 2 to 0.092 6. Therefore,the hierarchical objectoriented Multiple Endmember Spectral Mixture Model is suitable for the inversion of the vegetation coverage of the wetland.
引文
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[2]YANG Y X.Main characteristics,progress and prospect of international wetland science research[J].Progress in Geography,2002,21(2):111-120.
[3]POWELL R L,ROBERTS D A,DENNISON P E,et al.Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis:Manaus,Brazil[J].Remote Sensing of Environment,2007,106(2):253-267.
[4]BATESON A,CURTISS B.A method for manual endmember selection and spectral unmixing[J].Remote Sensing of Environment,1996,55(3):229-243.
[5]陶秋香,陶华学,张连蓬.线性混合光谱模型在植被高光谱遥感分类中的应用研究[J].勘察科学技术,2004(1):21-24.
[6]JOHNSON P E,SMITH M O,TAYLOR-GEORGE S,et al.A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures[J].Journal of Geophysical Research Solid Earth,1983,88(B4):3557-3561.
[7]GUTMANG,IGNATOV A.The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models[J].International Journal of Remote Sensing,1998,19(8):1533-1543.
[8]崔天翔,宫兆宁,赵文吉,等.不同端元模型下湿地植被覆盖度的提取方法:以北京市野鸭湖湿地自然保护区为例[J].生态学报,2013,33(4):1160-1171.
[9]廖春华,张显峰,刘羽.基于多端元光谱分解的干旱区植被覆盖度遥感反演[J].应用生态学报,2012,23(12):3243-3249.
[10]ROBERTS D A,GARDNER M,CHURCH R,et al.Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models[J].Remote Sensing of Environment,1998,65(3):267-279.
[11]SOMERS B,ASNER G P,TITS L,et al.Endmember variability in Spectral Mixture Analysis:A review[J].Remote Sensing of Environment,2011,115(7):1603-1616.
[12]RIDD M K.Exploring a V-I-S(vegetation-impervious surfacesoil)model for urban ecosystem analysis through remote sensing:comparative anatomy for cities[J].International Journal of Remote Sensing,1995,16(12):2165-2185.
[13]SMALL C,LU J W T.Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis[J].Remote Sensing of Environment,2006,100(4):441-456.
[14]SMALLC.Estimation of urban vegetation abundance by spectral mixture analysis[J].International Journal of Remote Sensing,2001,22(7):299-307.
[15]赵春晖,崔士玲,刘务.基于分层的多端元光谱解混算法[J].光电子·激光,2014,25(9):1830-1836.
[16]BASUKI T M,SKIDMORE A K,LAAKE P E V,et al.The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass[J].Geocarto International,2012,27(4):329-345.
[17]方金,黄晓东,王玮,等.青藏高原草地生物量遥感动态监测[J].草业科学,2011,28(7):1345-1351.