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Characterizing Urban Subpixel Composition Using Spectral Mixture Analysis
This chapter contains sections titled:
Introduction
Overview of SMA implementation
Two case studies
Conclusions
Acknowledgments
References
Characterizing Urban Subpixel Composition Using Spectral Mixture Analysis
This chapter contains sections titled:
Introduction
Overview of SMA implementation
Two case studies
Conclusions
Acknowledgments
References
Characterizing Urban Subpixel Composition Using Spectral Mixture Analysis
Yang, Xiaojun (Herausgeber:in) / Powell, Rebecca (Autor:in)
Urban Remote Sensing ; 111-128
15.04.2011
18 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
MESMA implementation ‐ quantifying V‐I‐S components of urban land cover , accuracy assessment ‐ one measure of model fit generated by SMA algorithm itself, and RMS error , MESMA tests multiple models, identifying ‐ one “best‐fit” model, pixel based on selection criteria and measure of goodness‐of‐fit , goal of EAR, representative spectrum ‐ material class in a library, and RMS error , SMA methodology ‐ case studies, flexibility of MESMA in mapping V‐I‐S components in different environments , SMA implementation ‐ method accounting for mixed pixels , multiple end member spectral analysis (MESMA) technique ‐ mapping urban V‐I‐S components , urban subpixel composition characterization ‐ using spectral mixture analysis , SMA models, set of end members ‐ representing basic spectral components of landscape , mapping fraction images
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