In this paper we address six problems we have encountered when sharpening multi-spectral imagery (MSI) using
panchromatic (PAN) images and describe methods we have developed to solve them. We also describe a PANsharpening
method that can be used for hyper-spectral data where the PAN-band does not cover all spectral bands. In
this paper we compare a number of currently used PAN-sharpening methods. The comparison is done (1) visually
creating true and false color composites and (2) compute their radiometric fidelity with the Wang-Bovik quality index.
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.
We are interested in using wide-field-of-view polarimetric imagers such as airborne POLDER (LOA, University of Lille) and AMPI (NASA) to study polarization signatures of surface targets. The airborne POLDER instrument has a field of view +/- 43° in along-track and +/- 51° cross-track. It records imagery through a rotating filter wheel with spectral filters, and in two spectral bands with linear polarization filters orientated at 0°, 60°, and 120° while flying at 70 m/s, generating images that need to be registered before spectra or the Stokes vectors can be computed. The atmospheric contributions, particularly in the short-wavelength visible bands, are anisotropic due to the scattering from molecules and aerosols, and the contrast is quite low, making automated image registration impossible. Thus, it is necessary to remove the polarized upwelling atmospheric contributions from the at-sensor radiances before the images can be registered, and target-leaving Stokes parameters can be derived. We accomplished the atmospheric correction by using the recently released vectorized version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6Sv) from Eric Vermote <i>et al</i>. 1997. We wrote a front-end in IDL to run 6Sv over a range of viewing zenith and azimuth angles. The resulting Stokes parameters are then interpolated to a grid of input viewing coordinates for the sensor. Next, the Stokes hemispherical path radiances are converted to match the data of the airborne POLDER instrument for the linear polarization angles of 0°, 60°, and 120°. The upwelling atmospheric intensities are subtracted from the respective polarimetric intensity images and the difference is divided by the transmission multiplied with the solar irradiance. This atmospheric correction significantly reduces the low-frequency variations in intensity in the images resulting from atmospheric scattering. The atmospherically corrected intensity images at 0°, 60°, and 120° are then used to calculate the Stokes parameter images in the usual fashion. From the Stokes parameter images we calculate the degree and azimuth of linear polarization images for the surface.