As described in Chapter 2 the RADAR instrument consists of a Synthetic Aperture Radar (SAR) imager, an altimeter, and a radiometer. The RADAR imaging mode provides low-to-high resolution SAR images (Elachi et al. 2004). The basic textures present in SAR images, especially the high-resolution ones, are affected by multiplicative speckle noise. In order to acquire qualitative data and extract the real surface properties despeckling algorithms should preserve the data (Walessa, 2000).
5.4.1 Speckle noise and despeckle filtering
First, it should be noted that despite the fact that the RADAR observations are almost unaffected by the atmospheric conditions, the speckle noise exists in all types of coherent imaging systems. Its main cause is the coherent processing of backscattered signals from multiple distributed targets (Franceschetti and Lanari, 1999) while it affects the resolution of the images (reduction) and the detectability of the target (Goodman, 1976; Bratsolis et al. 2012). Indeed, speckle noise is not only signal dependent but it is reduces the effectiveness of image reduction and thus it is spatially correlated. However, the speckle noise affects SAR images even more largely than other types of imaging causing misleading interpretations. On SAR images, the speckle noise overlays real structures and causes grey value variations even in homogenous image parts, making automatic segmentation difficult.
Many methods are used to remove speckle noise and produce more qualitative SAR data for the surface researches such as multiple-look (single radar) processing that averages and removes the speckle noise (e.g. Tso and Mather, 2009). Other processes include adaptive optics and non adaptive despeckle filtering (Franceschetti and Lanari, 1999). Here, I present a despeckle filtering technique, called the Total Sum Preserving Regularization (TSPR) filter, developed by my colleague Emmanuel Bratsolis (Bratsolis and Segelle, 2003) and used on Titan studies by our LESIA and University of Athens Team and presented in a publication in the Planetary and Space Science Journal (Bratsolis et al. 2012), which I co-authored. Georgios Bampasidis and myself applied this filter on SAR data of lakes, impacts and cryovolcanic candidates. I present the results of the latter in Chapter 6.
5.4.2 The Total Sum Preserving Regularization (TSPR) filter and segmentation of different regions of interest - Application on Cassini SAR
As mentioned hereabove, the Cassini/SAR data (Fig. 5.19a; 5.20a) used in this study of their retrieval are discussed in detail in Chapter 3. The TSPR filter is based on a membrane model Markov random field approximation with a Gaussian (Chitroub et al. 2002) conditional probability density function optimized by a synchronous local iterative method which provides a sum-preserving regularization for the pixel values of the image (Bratsolis and Sigelle, 2003). Moreover, the method is based on probabilistic methods and regards an image as a random element drawn from a pre-specified set of possible images. Hence, it preserves the mean values of local homogeneous regions and decreases the standard deviation up to six times (Fig. 5.19b; 5.20b).
In addition to the despeckling capabilities, the TSPR filter can be used as a tool for the extraction of geologically meaningful structural units by dividing an image into specific regions with common characteristics and thus isolating regions of interest (Fig. 5.19d;e; 5.20c;d;e). We are using a segmentation method to classify these regions (Bratsolis et al. 2012). The supervised method of minimum Euclidean distance uses the mean values of each member and calculates the Euclidean distance from each classified object to the nearest class segmenting the image into different regions of interest or different labels (Fig. 5.19d;e). The Otsu’s method (unsupervised segmentation) detects the optimum threshold of a histogram and separates the image into two (Fig. 5.20d) regions of interest.
Lakes
I present here the application of the filtering technique on two Cassini/SAR images that feature Titan lakes. The intended goal is to label regions in an image into three classes (dark lakes, granular lakes and the local background). First, a filtering technique is applied to obtain the restored image followed by a method of supervised segmentation, as explained hereabove. Figure 5.19a shows the original Cassini/SAR image, 5.19b the filtering result and 5.19c the ratio between the initial and filtered one. We follow up with the segmented image on Fig. 5.19d where we have selected three classes: the dark lakes (black), the granular lakes (grey) and the background (light grey) that correspond to blue, green and red respectively in the color-coded version in 5.19e.
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Fig. 5.19 – Filtering technique on Cassini/SAR original image PIA08630 showing several lakes. (a) The initial Cassini/SAR image (Image credit: NASA/JPL-CalTech/ASI); (b) The filtered Cassini/SAR using the TSPR filter (Bratsolis et al. 2012); (c) Image ratio between the initial (a) and the despeckled (b) image; (d) Segmented image after filtering; (e) Color-coded segmented image where the dark lake with low backscatter are illustrated with blue, the granular lakes with green and the local background is colored red.
I present hereafter another example of filtering and segmentation that we presented in the European Geosciences Union Congress in 2010. We use the Cassini SAR image of kissing lakes (PIA08740).
Fig. 5.20 – TSPR on the ‘kissing lakes’. (a) Original Cassini/SAR image PIA08740 (Image credit: NASA/JPL); (b) TSPR filter image; (c) Segmented image after filtering and before the optimum threshold; (d) Segmented image after filtering with the optimum threshold; (e) Segmented image after filtering and after the optimum threshold. With black label we can see the lakes and with white the local background.
My original work using this technique concerns the processing of Cassini/SAR images of the candidate cryovolcanic regions from which I extract information regarding their surface texture. These applications and results are summarized in Chapter 6.
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