# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 2 Methodology  
The principle behind all applications of SAR intensity data to shoreline extraction is based on the active nature of the sensor: smooth surfaces reflect incident radiation mainly in the specular direction, while rougher surfaces have a more diffuse scattering pattern. Therefore, backscattering level is negligible on calm water bodies with respect to land areas. This suggests immediately the use of a threshold approach to discriminate land from water within a SAR image. However, precise shoreline extraction from SAR images is a non trivial task since several factors influence the actual backscattering level, including surface roughness and soil moisture (for land areas), and the presence of capillary waves (for water bodies). Moreover, SAR images are affected by speckle noise that causes statistical fluctuations in the backscattering levels, hindering stable detection of threshold values.  
Many methods have been developed to deal with the shoreline extraction from SAR data. They generally consist of two steps: (i) despeckling; (ii) segmentation of the SAR image through the identification of the land-sea interface.  
Concerning the first step, it can be performed in different ways, i.e., through spatial or temporal filtering operators, or by exploiting polarization diversity. Since the aim of our research has been to design an operational EO system for the continuous monitoring of coastal areas, full polarized data have not been considered because the MapItaly program routinely collects only Stripmap HIMAGE single-polarization acquisitions. The adaptive despeckling algorithm proposed by [54] has been adopted in this study, since it has been demonstrated to provide a satisfactory trade-off between noise reduction and edge sharpness.  
The traditional techniques for land-sea image partitioning exploit edge-detection operators [55; 56], generally gradient-based, that, although effective, are not very robust, since they are not based on explicit mathematical models of speckle noise and edge extraction. The \"classic\" edge-detection algorithms are very sensitive to noise and, consequently, their effectiveness depends on the filters used to reduce noise to acceptable levels. However, too powerful filters can blur the edges; so, in order to obtain satisfactory results it is necessary to find a fair compromise between the noise reduction and edge sharpness. In addition, the choice of threshold values for use in edge-detection algorithmsrepresents a crucial point, due to the risk of including some spurious elements, not belonging to the land-sea interface.  
In this paper different segmentation techniques have been experimented for land-sea interface extraction from SAR images and an innovative algorithm, based on active contour analysis, has been tested [57]. A performance evaluation of the innovative algorithm has been carried out by comparing the extracted shorelines to GPS shorelines measured simultaneously with the SAR acquisition.  
Moreover, as part of the experimental investigation, a data processing chain, known as COSMO-Beach [58; 59], has been implemented to perform shoreline monitoring using both SAR amplitude and interferometric coherence information [60]. The developed system exploits remote sensed images from CSK constellation combined with detailed topographic and meteorological data collected in situ (Figure 1). The implemented procedure consists in (i) the selection of satellite images to be included in the processing, based on automatic checks of the marine weather conditions over the target area, (ii) the morphological classification of the coastline, (iii) the segmentation of SAR data for land-sea interface identification and (iv) the final tidal correction. Following these steps, a diachronic analysis of the extracted shorelines can be achieved. Hereafter, the main processing steps are outlined.