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    中国百强科技报刊

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    中国高校百佳科技期刊

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    Volume 40 Issue 8
    Aug.  2015
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    Article Contents
    Chen Lei, Chen Shengbo, Yang Qian, Ma Ming, Liu Daofei, 2015. A Spectral Mixture Model Based on Spectral Spatial Character of Measured Hyperspectral Data. Earth Science, 40(8): 1359-1364. doi: 10.3799/dqkx.2015.118
    Citation: Chen Lei, Chen Shengbo, Yang Qian, Ma Ming, Liu Daofei, 2015. A Spectral Mixture Model Based on Spectral Spatial Character of Measured Hyperspectral Data. Earth Science, 40(8): 1359-1364. doi: 10.3799/dqkx.2015.118

    A Spectral Mixture Model Based on Spectral Spatial Character of Measured Hyperspectral Data

    doi: 10.3799/dqkx.2015.118
    • Received Date: 2015-03-05
    • Publish Date: 2015-08-01
    • In order to enhance the estimation of mixed spectral model, the equidistant/ homalographic model is established to analyze the spectral spatial character to simulate the mixture spectra. Based on the reflex platform and FieldSpec 3 Hi-Res portable spectrum instrument, the equidistant/ homalographic experiment, which takes the effect of distance between optical fiber probe and detected endmember into account, was designed to acquire the mixed spectral reflectance of calcite and green leaf. The measured mixed spectra analysis shows the weight coefficients of distribution change with the distance between the detected endmember and the probe is in a Gauss distribution. Compared with the linear spectral mixture model and improved linear spectral model, the results simulated by the equidistant/ homalographic model is 1.20% greater in similarity and 7.78% lower in RMSE. Considering the influence exerted by spectral spatial structure on mixed spectral simulation, the equidistant/ homalographic model proves to improve the accuracy of mixed spectral simulation and a new method for unmixing the mixed pixel of hyperspectral data.

       

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    • Borel, C.C., Gerstl, S.A.W., 1994. Nonlinear Spectral Mixing Models for Vegetative and Soil Surfaces. Remote Sensing of Environment, 47(3): 403-416. doi: 10.1016/0034-4257(94)90107-4
      Chen, S.B., Chen, L., Liu, Y.L., et al., 2013. Experimental Simulation on Mixed Spectra of Leaves and Calcite for Inversion of Carbonate Minerals from EO-1 Hyperion Data. GIS Science & Remote Sensing, 50(6): 690-703. doi: 10.1080/15481603.2013.866792
      Chen, S.B., Li, X.L., Chen, L., 2014. Study on Inversion of Soil Heavy Metal Elements Concentrations in Stream Sediments by In-Situ Hyperspectral Measurements. Journal of Jilin University(Earth Science Edition), 44(4): 1388-1394 (in Chinese with English abstract). http://www.researchgate.net/publication/285993021_Study_on_inversion_of_soil_heavy_metal_elements_concentrations_in_stream_sediments_by_in-situ_hyperspectral_measurements
      Du, P.J., Lin, H., Sun, D.X., et al., 2006. On Progress of Support Vector Machine based Hyper Spectral RS Classification. Bulletin of Surveying and Mapping, (12): 37-40 (in Chinese). doi: 10.1080/01431160500242515
      Knipling, E.B., 1970. Physical and Physiological Basis for the Reflectance of Visible and Near-Infrared Radiation from Vegetation. Remote Sensing of Environment, 1(3): 155-159. doi: 10.1016/S0034-4257(70)80021-9
      Li, X.W., Strahler, A.H., 1985. Geometric-Optical Modeling of a Conifer Forest Canopy. IEEE Trans. Geosci. Remote Sensing, 23(5): 705-721. doi: 10.1109/tgrs.1985.289389
      Li, X.W., Strahler, A.H., 1986. Geometric-Optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy. IEEE Trans. Geosci. Remote Sensing, 24: 906-919. http://ieeexplore.ieee.org/document/4072562/citations
      Lü, J., Liu, X.N., 2012. Hyperspectral Remote Sensing Estimation Model for Cd Concentration in Rice Using Support Vector Machines. Journal of Applied Sciences, 30(1): 105-110 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-YYKX201201018.htm
      Miao, X., Gong, P., Swope, S., et al., 2006. Estimation of Yellow Starthistle Abundance through CASI-2 Hyperspectral Imagery Using Linear Spectral Mixture Models. Remote Sensing of Environment, 101(3): 329-341. doi: 10.1016/j.rse.2006.01.006
      Piwowar, J.M., Peddle, D.R., LeDrew, E.F., 1998. Temporal Mixture Analysis of Arctic Sea Ice Imagery: A New Approach for Monitoring Environmental Change. Remote Sensing of Environment, 63(3): 195-207. doi: 10.1016/j.rse.2004.10.008
      Raksuntorn, N., Du, Q., 2010. Nonlinear Spectral Mixture Analysis for Hyperspectral Imagery in an Unknown Environment. IEEE Geoscience and Remote Sensing Letters, 7(4): 836-840. doi: 10.1109/lgrs.2010.2049334
      Roberts, D.A., Adams, J.B., Smith, M.O., 1990. Predicted Distribution of Visible and Near-Infrared Radiant Flux above and below a Transmittant Leaf. Remote Sensing of Environment, 34(1): 1-17. doi: 10.1016/0034-4257(90)9008006
      Savitzky, A., Golay, M.J.E., 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8): 1627-1639. doi: 10.1021/ac60214a047
      Yang, C.H., Everitt, J.H., Du, Q., 2010. Applying Linear Spectral Unmixing to Airborne Hyperspectral Imagery for Mapping Yield Variability in Grain Sorghum and Cotton Fields. Journal of Applied Remote Sensing, 4(1): 041887. doi: 10.1117/1.3484252
      Zhu, F., Gong, H.L., Sun, T.L., et al., 2013. Study on the Nonlinear Characteristics of the Mixed Pixel's Reflectance in Hyperspectral Space. Spectroscopy and Spectral Analysis, 33(3): 737-740 (in Chinese with English abstract). http://europepmc.org/abstract/med/23705444
      Zhu, L., Xu, J.F., Huang, J.F., et al., 2008. Study on Hyperspectral Estimation Model of Crop Vegetation Cover Percentage. Spectroscopy and Spectral Analysis, 28(8): 1827-1831 (in Chinese with English abstract). http://europepmc.org/abstract/MED/18975813
      陈圣波, 李鑫龙, 陈磊, 2014. 基于地面实测光谱的水系沉积物重金属含量反演. 吉林大学学报(地球科学版), 44(4): 1388-1394. https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201404035.htm
      杜培军, 林卉, 孙敦新, 等, 2006. 基于支持向量机的高光谱遥感分类进展. 测绘通报, (12): 37-40. doi: 10.3969/j.issn.0494-0911.2006.12.011
      吕杰, 刘湘南, 2012. 利用支持向量机构建水稻镉含量高光谱预测模型. 应用科学学报, 30(1): 105-110. doi: 10.3969/j.issn.0255-8297.2012.01.016
      朱蕾, 徐俊锋, 黄敬峰, 等, 2008. 作物植被覆盖度的高光谱遥感估算模型. 光谱学与光谱分析, 28(8): 1827-1831. doi: 10.3964/j.issn.1000-0593.2008.08.032
      朱锋, 宫辉力, 孙天琳, 等, 2013. 高光谱空间中混合象元非线性反射特征研究. 光谱学与光谱分析, 33(3): 737-740. doi: 10.3964/j.issn.1000-0593(2013)03-0737-04
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