Paper
2 April 2008 Utility of polarimetric imagery as ancillary data for thematic classification as predicted by using measures of spectral separability
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Abstract
1991 STS-43 Shuttle polarimetric imagery was used to calculate a degree of linear polarization (DoLP) image, registered to a 1994 NW Madagascar Landsat 5 scene. The Jeffries-Matusita (J-M) distance and Transformed Divergence (TD) were calculated for every class-pair of a 13-class ISODATA classification, using 6 TM (Thematic Mapper) bands, and TM bands plus DoLP. Separabilities were also calculated using just TM bands 7, 4, and 2 and compared against them plus DoLP. There was a small, consistent increase in spectral separability for all class-pairs with the addition of DoLP. A similar experiment was done using 1999 Landsat 7 imagery covering the area of the 1994 NASA BOREAS experiment. The separabilities were calculated for just a 742 combination and 742 plus DoLP created from the airborne POLDER sensor. The maximum increase in separability provided by DoLP was nearly 5-fold greater than for the Madagascar imagery. The separabilities were compared also for bands 742 plus DoLP with 742 plus band 1 and 742 plus band 5. As with band 1, band 5 improved more class-pair separabilities than DoLP, but the average amount of improvement was more than 4-times greater for DoLP for those class-pairs that saw improvement, versus those that were better separated by band 5. The J-M distance predicts better classification performance for DoLP compared to bands 1 and 5, while the TD suggests slightly better performance for band 5 over DoLP or band 1.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Clyde H. Spencer "Utility of polarimetric imagery as ancillary data for thematic classification as predicted by using measures of spectral separability", Proc. SPIE 6972, Polarization: Measurement, Analysis, and Remote Sensing VIII, 69720F (2 April 2008); https://doi.org/10.1117/12.772036
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KEYWORDS
Earth observing sensors

Landsat

Polarimetry

Polarization

Vegetation

Image classification

Aerospace engineering

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