An investigation of spectral metrics in hyperspectral image preprocessing for classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

The paper investigates the efficiency of spectral metrics when used in spectral screening of hyperspectral imagery. Spectral screening is the technique of selecting from the data a subset of representative spectra that can be used in further processing. The subset is formed such that any two spectra in it are dissimilar and, for any spectrum in the original image cube, there is a similar spectrum in the subset. The similarity can be described through various spectral distances and can be controlled by a threshold value. The spectral screening can be improved by associating to each spectrum in the subset a weighing factor proportional to the number of spectra in the original image that are similar to it. Following its generation, the screened subset is used in further computations instead of the full data. The resulting processing mappings are then applied to the data. The investigation has focused on the comparison between Spectral Angle (SA), Spectral Correlation Angle (SCA), Spectral Information Divergence (SID), and spectral gradient angle (SGA) in terms of accuracy of the results and speedup obtained. Spectral screening is performed prior to Principal Component Analysis. The PCA result is next extended to the full data. To quantify the accuracy we rely on unsupervised classification of the resulting processed data. Results from experiments on AVIRIS data show that no significant classification accuracy is recorded while the main processing was done on a subset representing only a very small fraction of the original data size.

Original languageEnglish
Title of host publicationAmerican Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global
Subtitle of host publicationFrom Your Neighborhood to the Whole Planet
Pages942-951
Number of pages10
StatePublished - 1 Dec 2005
EventAnnual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet - Baltimore, MD, United States
Duration: 7 Mar 200511 Mar 2005

Publication series

NameAmerican Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet
Volume2

Other

OtherAnnual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet
CountryUnited States
CityBaltimore, MD
Period7/03/0511/03/05

Fingerprint

Screening
Processing
Weighing
Set theory
Principal component analysis
AVIRIS
unsupervised classification
principal component analysis
imagery
divergence
Experiments
screening
experiment

Cite this

Robila, S. (2005). An investigation of spectral metrics in hyperspectral image preprocessing for classification. In American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet (pp. 942-951). (American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet; Vol. 2).
Robila, Stefan. / An investigation of spectral metrics in hyperspectral image preprocessing for classification. American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet. 2005. pp. 942-951 (American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet).
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abstract = "The paper investigates the efficiency of spectral metrics when used in spectral screening of hyperspectral imagery. Spectral screening is the technique of selecting from the data a subset of representative spectra that can be used in further processing. The subset is formed such that any two spectra in it are dissimilar and, for any spectrum in the original image cube, there is a similar spectrum in the subset. The similarity can be described through various spectral distances and can be controlled by a threshold value. The spectral screening can be improved by associating to each spectrum in the subset a weighing factor proportional to the number of spectra in the original image that are similar to it. Following its generation, the screened subset is used in further computations instead of the full data. The resulting processing mappings are then applied to the data. The investigation has focused on the comparison between Spectral Angle (SA), Spectral Correlation Angle (SCA), Spectral Information Divergence (SID), and spectral gradient angle (SGA) in terms of accuracy of the results and speedup obtained. Spectral screening is performed prior to Principal Component Analysis. The PCA result is next extended to the full data. To quantify the accuracy we rely on unsupervised classification of the resulting processed data. Results from experiments on AVIRIS data show that no significant classification accuracy is recorded while the main processing was done on a subset representing only a very small fraction of the original data size.",
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Robila, S 2005, An investigation of spectral metrics in hyperspectral image preprocessing for classification. in American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet. American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet, vol. 2, pp. 942-951, Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet, Baltimore, MD, United States, 7/03/05.

An investigation of spectral metrics in hyperspectral image preprocessing for classification. / Robila, Stefan.

American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet. 2005. p. 942-951 (American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The paper investigates the efficiency of spectral metrics when used in spectral screening of hyperspectral imagery. Spectral screening is the technique of selecting from the data a subset of representative spectra that can be used in further processing. The subset is formed such that any two spectra in it are dissimilar and, for any spectrum in the original image cube, there is a similar spectrum in the subset. The similarity can be described through various spectral distances and can be controlled by a threshold value. The spectral screening can be improved by associating to each spectrum in the subset a weighing factor proportional to the number of spectra in the original image that are similar to it. Following its generation, the screened subset is used in further computations instead of the full data. The resulting processing mappings are then applied to the data. The investigation has focused on the comparison between Spectral Angle (SA), Spectral Correlation Angle (SCA), Spectral Information Divergence (SID), and spectral gradient angle (SGA) in terms of accuracy of the results and speedup obtained. Spectral screening is performed prior to Principal Component Analysis. The PCA result is next extended to the full data. To quantify the accuracy we rely on unsupervised classification of the resulting processed data. Results from experiments on AVIRIS data show that no significant classification accuracy is recorded while the main processing was done on a subset representing only a very small fraction of the original data size.

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Robila S. An investigation of spectral metrics in hyperspectral image preprocessing for classification. In American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet. 2005. p. 942-951. (American Society for Photogrammetry and Remote Sensing - Annual Conference 2005 - Geospatial Goes Global: From Your Neighborhood to the Whole Planet).