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Incidence angle correction of AirSAR data to facilitate land-cover classification

Menges, CH, Hill, GJE, Ahmad, W and Van Zyl, JJ (2001). Incidence angle correction of AirSAR data to facilitate land-cover classification. Photogrammetric Engineering and Remote Sensing,67(4):479-489.

Document type: Journal Article
Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 9 times in Scopus Article | Citations
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ISI LOC 000167882800009
Title Incidence angle correction of AirSAR data to facilitate land-cover classification
Author Menges, CH
Hill, GJE
Ahmad, W
Van Zyl, JJ
Journal Name Photogrammetric Engineering and Remote Sensing
Publication Date 2001
Volume Number 67
Issue Number 4
ISSN 0099-1112   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-0035088763
Start Page 479
End Page 489
Total Pages 11
Place of Publication Bethesda, MD, USA
Publisher American society for Photogrammetry and Remote Sensing
HERDC Category C1 - Journal Article (DEST)
Abstract Three image-based methods for correcting the effect of changes in incidence angle on backscatter data are proposed and evaluated for AirSAR data of a coastal tropical savanna landscape in Australia's Northern Territory. These correction methods require little field knowledge and do not assume a linear relationship between SAR backscatter and incidence angle. The correction methods are applied to five independent components of the Stokes Matrix. The results are evaluated using an existing land-cover classification to extract mean backscatter values for individual land-cover classes before and after the correction procedures. It is shown, for the vegetation communities involved, that the slope method provides a successful correction for the amplitude components of the backscatter data. The real and imaginary parts of the co-polarized returns are best corrected using the LUT method. The proposed correction procedure allowed the use of a maximum-likelihood classification for the discrimination of ten land-cover types with an overall accuracy of 87 percent across the complete swath width of the AirSAR data.
Keywords radar backscatter
soil-moisture
boreal forests
pine stands
sar images
bare soil
biomass
model
vegetation
 
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