Abstract
This 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 spectra such that any two spectra in the subset are dissimilar and, for any spectra in the original image cube, there is a similar spectra in the subset. The method can use various spectral metrics to characterize the similarity and can be seen as a data reduction step if the resulting subset is used in further computations instead of the full data. The investigation has focused on the comparison between spectral angle and spectral correlation angle in terms of efficiency of the results and speedup obtained as well as in empirically identifying the best distance threshold to be used when reducing the data. The techniques were tested on Hyperion imagery when using PCA and show promising speedup.
| Original language | English |
|---|---|
| Pages | 3233-3236 |
| Number of pages | 4 |
| State | Published - 1 Dec 2004 |
| Event | 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 - Anchorage, AK, United States Duration: 20 Sep 2004 → 24 Sep 2004 |
Other
| Other | 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 |
|---|---|
| Country | United States |
| City | Anchorage, AK |
| Period | 20/09/04 → 24/09/04 |
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Keywords
- Data reduction
- Hyperspectral images
- Spectral angle
- Spectral correlation angle
- Spectral metrics
Cite this
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An analysis of spectral metrics for hyperspectral image processing. / Robila, Stefan.
2004. 3233-3236 Paper presented at 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004, Anchorage, AK, United States.Research output: Contribution to conference › Paper › Research › peer-review
TY - CONF
T1 - An analysis of spectral metrics for hyperspectral image processing
AU - Robila, Stefan
PY - 2004/12/1
Y1 - 2004/12/1
N2 - This 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 spectra such that any two spectra in the subset are dissimilar and, for any spectra in the original image cube, there is a similar spectra in the subset. The method can use various spectral metrics to characterize the similarity and can be seen as a data reduction step if the resulting subset is used in further computations instead of the full data. The investigation has focused on the comparison between spectral angle and spectral correlation angle in terms of efficiency of the results and speedup obtained as well as in empirically identifying the best distance threshold to be used when reducing the data. The techniques were tested on Hyperion imagery when using PCA and show promising speedup.
AB - This 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 spectra such that any two spectra in the subset are dissimilar and, for any spectra in the original image cube, there is a similar spectra in the subset. The method can use various spectral metrics to characterize the similarity and can be seen as a data reduction step if the resulting subset is used in further computations instead of the full data. The investigation has focused on the comparison between spectral angle and spectral correlation angle in terms of efficiency of the results and speedup obtained as well as in empirically identifying the best distance threshold to be used when reducing the data. The techniques were tested on Hyperion imagery when using PCA and show promising speedup.
KW - Data reduction
KW - Hyperspectral images
KW - Spectral angle
KW - Spectral correlation angle
KW - Spectral metrics
UR - http://www.scopus.com/inward/record.url?scp=15944378854&partnerID=8YFLogxK
M3 - Paper
SP - 3233
EP - 3236
ER -