Influence from polarized galactic background noise on L-band measurements of the sea surface salinity
The polarimetric EMIRAD radiometer, based on novel digital down conversion and detection techniques, has been installed on a C-130 aircraft from the Royal Danish. Air Force during the L-band Ocean Salinity Airborne Campaign (LOSAC) in 2001 and 2003. Full 360/spl deg/ circle flight patterns around the same target area as well as clover leaf patterns have been measured, and both provide an azimuth signature of the ocean at a constant incidence angle. The resulting azimuth signatures show significant variations in all the three first Stokes parameters, and a correlation is found between the downwelling galactic background signal and the measured results. The measured 3/sup rd/ Stokes parameter has variations of the same order of magnitude as the two linear polarizations, and to verify this result, an experiment for direct observation of the sky over long time is set up. This experiment confirms the presence of a polarized galactic background signal, and conclusions are made with respect to the necessity for polarimetric corrections in future measurements over the sea at L-band.
Comparison of model prediction with measurements of galactic background noise at L-band
The spectral window at L-band (1.413 GHz) is important for passive remote sensing of surface parameters such as soil moisture and sea surface salinity that are needed to understand the hydrological cycle and ocean circulation. Radiation from celestial sources (mostly galactic) is strong in this window, and an accurate accounting of this background radiation is often needed for calibration. This paper presents a comparison of the background radiation predicted by a model developed from modern radio astronomy measurements with measurements made with several modern L-band remote sensing radiometers. The comparison validates the model and illustrates the magnitude of the correction necessary in remote sensing applications.
Visualization and unsupervised classification of changes in multispectral satellite imagery
The statistical techniques of multivariate alteration detection, minimum/maximum autocorrelation factors transformation, expectation maximization and probabilistic label relaxation are combined in a unified scheme to visualize and to classify changes in multispectral satellite data. The methods are demonstrated with an example involving bitemporal LANDSAT TM imagery.