A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability
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[1] Gregory Asner,et al. Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis , 2000, IEEE Trans. Geosci. Remote. Sens..
[2] Jiancheng Luo,et al. Applying spectral mixture analysis for large-scale sub-pixel impervious cover estimation based on neighbourhood-specific endmember signature generation , 2015 .
[3] A. McBratney,et al. Choosing functions for semi‐variograms of soil properties and fitting them to sampling estimates , 1986 .
[4] Yingbin Deng,et al. Segmentation-based and rule-based spectral mixture analysis for estimating urban imperviousness , 2015 .
[5] Chein-I Chang,et al. Weighted abundance-constrained linear spectral mixture analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[6] Alan R. Gillespie,et al. Vegetation in deserts. I - A regional measure of abundance from multispectral images. II - Environmental influences on regional abundance , 1990 .
[7] Margaret E. Gardner,et al. Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .
[8] C. Small. Estimation of urban vegetation abundance by spectral mixture analysis , 2001 .
[9] Chunyang He,et al. Prior-knowledge-based spectral mixture analysis for impervious surface mapping , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[10] D. Lobell,et al. A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .
[11] M. Ridd. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .
[12] Changshan Wu,et al. A geostatistical temporal mixture analysis approach to address endmember variability for estimating regional impervious surface distributions , 2016 .
[13] D. Lobell,et al. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. , 2000 .
[14] Caiyun Zhang,et al. Mapping urban land cover types using object-based multiple endmember spectral mixture analysis , 2014 .
[15] Alan T. Murray,et al. Estimating impervious surface distribution by spectral mixture analysis , 2003 .
[16] J. Ross Quinlan,et al. Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..
[17] Changshan Wu,et al. Phenology-based temporal mixture analysis for estimating large-scale impervious surface distributions , 2014 .
[18] Peter M. Atkinson,et al. Geostatistics and remote sensing , 1998 .
[19] Changshan Wu,et al. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .
[20] Ben Somers,et al. A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems , 2009 .
[21] D. Lobell,et al. View angle effects on canopy reflectance and spectral mixture analysis of coniferous forests using AVIRIS , 2002 .
[22] Nirmal Keshava,et al. A Survey of Spectral Unmixing Algorithms , 2003 .
[23] S. Tompkins,et al. Optimization of endmembers for spectral mixture analysis , 1997 .
[24] Jiang Li,et al. Correction to "Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals" , 2004 .
[25] Changshan Wu,et al. Incorporating land use land cover probability information into endmember class selections for temporal mixture analysis , 2015 .
[26] Benoit Rivard,et al. Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[27] Mario Winter,et al. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.