DocumentCode :
1158590
Title :
Knowledge-based classification of polarimetric SAR images
Author :
Pierce, Leland E. ; Ulaby, Fawwaz T. ; Sarabandi, Kamal ; Dobson, M. Craig
Author_Institution :
Radiation Lab., Michigan Univ., Ann Arbor, MI, USA
Volume :
32
Issue :
5
fYear :
1994
fDate :
9/1/1994 12:00:00 AM
Firstpage :
1081
Lastpage :
1086
Abstract :
In preparation for the flight of the Shuttle Imaging Radar-C (SIR-C) on board the Space Shuttle in the spring of 1994, a level-1 automatic classifier was developed on the basis of polarimetric SAR images acquired by the JPL AirSAR system. The classifier uses L- and C-Band polarimetric SAR measurements of the imaged scene to classify individual pixels into one of four categories: tall vegetation (trees), short vegetation, urban, or bare surface, with the last category encompassing water surfaces, bare soil surfaces, and concrete or asphalt-covered surfaces. The classifier design uses knowledge of the nature of radar backscattering from surfaces and volumes to construct appropriate discriminators in a sequential format. The classifier, which was developed using training areas in a test site in Northern Michigan, was tested against independent test areas in the same test site and in another site imaged three months earlier. Among all cases and all categories, the classification accuracy ranged between 91% and 100%
Keywords :
geophysical techniques; geophysics computing; image recognition; knowledge based systems; remote sensing by radar; synthetic aperture radar; 1.25 GHz; 5.3 GHz; C-Band; L-band UHF SHF; SAR imaging; SIR-C; Shuttle Imaging Radar-C; Space Shuttle; computer method; discriminator; expert system; geophysical measurement technique; knowledge based image classification; land surface; level-1 automatic classifier; polarimetric SAR image; radar backscattering; radar remote sensing; synthetic aperture radar; terrain mapping; trees; urban; vegetation; Classification tree analysis; Concrete; Layout; Pixel; Radar imaging; Soil measurements; Space shuttles; Springs; Testing; Vegetation mapping;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/36.312896
Filename :
312896
Link To Document :
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