Examining the Effectiveness of Spectrally Transformed SMA in Urban Environments

Spectral transformation has been applied to address the spectral variability in spectral mixture analysis. However, there is not a study addressing the necessity and applicability of transformed models. This article, therefore, aims to answer two questions: whether significantly different results wi...

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Bibliographic Details
Published in:Photogrammetric engineering and remote sensing 2019-07, Vol.85 (7), p.521-528
Main Authors: Deng, Yingbin, Wu, Changshan
Format: Article
Language:English
Online Access:Get full text
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Summary:Spectral transformation has been applied to address the spectral variability in spectral mixture analysis. However, there is not a study addressing the necessity and applicability of transformed models. This article, therefore, aims to answer two questions: whether significantly different results will be generated through applying a spectral transformation, and which spectral transformation performs better in urban environments. In particular, 26 spectrally transformed schemes were examined in three cities. Results of paired-sample t tests demonstrated that normalized spectral mixture analysis performed significantly better than the untransformed scheme in all three study areas. Derivative analysis, independent component analysis, and minimum noise fraction outperformed the untransformed scheme in one or two study areas but underperformed in others. Other schemes are unnecessary, as they have significantly lower accuracy compared to the untransformed scheme.
ISSN:0099-1112
DOI:10.14358/PERS.85.7.521