Open AccessExamining the Effectiveness of Spectrally Transformed SMA in Urban Environments
Author(s) -
Yingbin Deng,
Changshan Wu
Publication year - 2019
Publication title -
photogrammetric engineering and remote sensing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 127
eISSN - 2374-8079
pISSN - 0099-1112
DOI - 10.14358/pers.85.7.521
Subject(s) - transformation (genetics) , spectral analysis , noise (video) , scheme (mathematics) , sma* , geography , mathematics , computer science , algorithm , physics , artificial intelligence , mathematical analysis , spectroscopy , chemistry , biochemistry , quantum mechanics , image (mathematics) , gene
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.