Titan's surface from Cassini SAR Principal Component Analysis (PCA) from orthorectified and non-orthorectified image pairs

Details Titan's surface from Cassini SAR Principal Component Analysis (PCA) from orthorectified and non-orthorectified image pairs Titan's surface from Cassini SAR Principal Component Analysis (PCA) from orthorectified and non-orthorectified image pairs

May 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agusm.p11a..06p&link_type=abstract

American Geophysical Union, Spring Meeting 2009, abstract #P11A-06

Mathematics

Logic

5400 Planetary Sciences: Solid Surface Planets, 5464 Remote Sensing

Scientific paper

The objective of this study is to analyze the suitability of principal component analysis (PCA) of Cassini SAR images to enhance interpretability of surface features for geological mapping on Titan. With repeated flybys around Titan, several areas are now covered by overlapping flybys giving the opportunity to map the surface with different incidence angle and at times opposite look direction. We extract principal components (PCs) from overlapping Cassini SAR scenes, acquired in like- and opposite-look direction using both orthorectified (ortho) image pairs, where the distortion due to topographical variations in the surface are removed using digital topographic models (DTMs), and also using non-orthorectified (non-ortho) images pairs. The use of PCA minimizes the data redundancy inherent in the Cassini SAR scenes and creates component images that are characterized by a linear combination of the input data. The feature-oriented principal components selection (FPCS) method has been applied to examine the PCA eigenvector loadings and to understand which principal component images concentrate and enhance information directly related to the backscattering response of specific target area such the dunes area in T8/T21 and T8/T41 overlap. The application of the method to the selected areas shows that the non-ortho PC2 image, the second derived principal component image, optimizes the topographic perception inherent in the radar images and retains information related to radar backscattering response to surface characteristics providing an excellent base for mapping. More importantly, an interesting finding has emerged about the ortho-image PC2 as it seems to define systematic patterns in which the dunes, especially in some areas, are darker. Considering that the ortho-images have been corrected for the mean Titan backscatter law this means the dunes might follow a different law. We have seen differences between dune- nondune areas so far, but we will also be using the technique to look for differences in scattering law behavior for other types of surfaces. We are investigating and testing different backscattering law scenario along with incidence angles changes across the images through the systematic variation observed in the PC2 ortho-images. The stereo coverage on Titan is mainly from pairs of images, but there are a few areas with up to 4 overlapping images. Applying PCA to these areas might show something beyond the simple sum and difference from the use of only 2 input images, and might help in a better understanding of the backscattering law scenario.

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