Enhanced Processing and Analysis of Cassini SAR Images of Titan

SAR images suffer from speckle noise, which hinders interpretation and quantitative analysis. We have adapted a non-local algorithm for de-noising images using an appropriate multiplicative noise model [1] for analysis of Cassini SAR images. We illustrate some examples here that demonstrate the improvement of landform interpretation by focusing on transport processes at Titan's surface. Interpretation of the geomorphic features is facilitated (Figure 1); including revealing details of the channels incised into the terrain, shoreline morphology, and contrast variations in the dark, liquid covered areas. The latter are suggestive of sub-marine channels and gradients in the bathymetry. Furthermore, substantial quantitative improvements are possible. We show that a derived Digital Elevation Model from radargrammetry [2] using the de-noised images is obtained with a greater number of matching points (up to 80%) and a better correlation (59% of the pixels give a good correlation in the de-noised data compared with 18% in the original SAR image). An elevation hypsogram of our enhanced DEM shows evidence that fluvial and/or lacustrine processes have affected the topographic distribution substantially. Dune wavelengths and interdune extents are more precisely measured. Finally, radarclinometry technics applied to our new data are more accurate in dunes and mountainous regions. [1] Deledalle C-A., et al., 2009, Weighted maximum likelihood denoising with iterative and probabilistic patch-based weights, Telecom Paris. [2] Kirk, R.L., et al., 2007, First stereoscopic radar images of Titan, Lunar Planet. Sci., XXXVIII, Abstract #1427, Lunar and Planetary Institute, Houston