Remote sensing of burned areas via PCA, Part 2: SVD-based PCA using MODIS and Landsat data

Remote sensing of burned areas via PCA, Part 2: SVD-based PCA using MODIS and Landsat data Background: Singular value decomposition (SVD), as an alternative solution to principal components analysis (PCA), may enhance the spectral profile of burned areas in satellite image composites. Methods: In this regard, we combine the pre-processing options of centering, non-centering, scaling, and non-scaling the input multi-spectral data, prior to the matrix decomposition, and treat their combinations as four different SVD-based PCA versions. Using both unitemporal and bi-temporal data sets, we test all four combinations to derive principal components. We assess the effects of the transformations based on multiresponse permutation procedures and quantify the enhanced spectral separability between burned areas and other major land cover classes via the Jeffries-Matusita metric. Lastly, we evaluate visually and numerically all principal components and select a subset of interest. Results: The best transformation for the subset of selected components, is the uncentered-unscaled one. Conclusions: The results indicate that an uncentered and unscaled SVD may improve the spectral separability of burned areas in some of the higher order components. Keywords: PCA, EVD, SVD, Mean-centering, Scaling, Burned area mapping, MODIS, Landsat5 TM, Free open source software Background latter case, it is well noted that burned surfaces are absent In the article “Remote sensing of burned areas via PCA, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Geospatial Data, Software and Standards Springer Journals

Remote sensing of burned areas via PCA, Part 2: SVD-based PCA using MODIS and Landsat data

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Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s)
Subject
Geography; Geographical Information Systems/Cartography; Information Systems Applications (incl.Internet); Communications Engineering, Networks
eISSN
2363-7501
D.O.I.
10.1186/s40965-017-0029-0
Publisher site
See Article on Publisher Site

Abstract

Background: Singular value decomposition (SVD), as an alternative solution to principal components analysis (PCA), may enhance the spectral profile of burned areas in satellite image composites. Methods: In this regard, we combine the pre-processing options of centering, non-centering, scaling, and non-scaling the input multi-spectral data, prior to the matrix decomposition, and treat their combinations as four different SVD-based PCA versions. Using both unitemporal and bi-temporal data sets, we test all four combinations to derive principal components. We assess the effects of the transformations based on multiresponse permutation procedures and quantify the enhanced spectral separability between burned areas and other major land cover classes via the Jeffries-Matusita metric. Lastly, we evaluate visually and numerically all principal components and select a subset of interest. Results: The best transformation for the subset of selected components, is the uncentered-unscaled one. Conclusions: The results indicate that an uncentered and unscaled SVD may improve the spectral separability of burned areas in some of the higher order components. Keywords: PCA, EVD, SVD, Mean-centering, Scaling, Burned area mapping, MODIS, Landsat5 TM, Free open source software Background latter case, it is well noted that burned surfaces are absent In the article “Remote sensing of burned areas via PCA,

Journal

Open Geospatial Data, Software and StandardsSpringer Journals

Published: Aug 24, 2017

References

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