Abstract. An essential step in the characterization of surface materials using hyperspectral image analysis is image classification using endmembers. Spectral unmixing is the best method for hyperspectral image classification. This method assumes that the pixel-topixel variability in scene results from varying proportions of spectral endmembers. Spectral endmembers can be derived from the imagery or measurements in the laboratory or field. The primary objective of this paper was to assess the ability of extracted endmembers against some different solutions for extraction of endmembers for hyperspectral image classification. In this paper, we compared the Pixel Purity Index (PPI) and the Sequential Maximum Angle Convex Cone (SMACC) as two popular methods of endmember extraction with library and field spectral. We used spectral information divergence for detection desirable endmembers from field spectral. For accuracy assessment of spectral mixture analysis and production of endmember abundance images for each of methods, the linear spectral unmixing algorithm is used. After a comparison between the results of these methods, it has been verified that field spectral have a better classification result in comparing with other endmember extraction methods. Also, the PPI has reliable results as an automatic endmember extraction method in comparing.
Abstract. Satellite Interferometric Synthetic Aperture Radar (InSAR) Signals are often intensively contaminated by atmospheric delays. The atmospheric conditions especially its water vapor content significantly varies in space and with time. Therefore, it is essential to characterize the atmospheric variations in order to mitigate these effects by appropriate means. In the topographic case, for high-resolution DEM generation, the errors may be decreased by choosing interferometric pairs with relatively long baselines, as the error magnitude is inversely dependent on the perpendicular component of the interferometer baseline. For InSAR DEM quality estimation against tropospheric corrected one, we use the GPS measurements to create the reference DEM in the region. The preliminary results show that the corrected DEM by the phase gradient method has the highest correlation with the ground-based observations and the InSAR DEM which is corrected by phase gradient stacking method for water vapor effect in the troposphere has the best accuracy, ranging from 0.07 ~ 5.81 m, while the InSAR original DEM has an accuracy of 0.29 ~ 14.62 m.
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