# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 1 Introduction
The existing approaches for shoreline extraction from SAR images are classified into two types: radiometric approach, certainly the most used, and interferometric approach. The first approach attempts to locate the discontinuities present in significant intensity values of the images [33; 34; 35; 36] whereas the second exploits the loss of coherence typical of water-covered areas [37; 38]. To overcome the issues related to the presence of noise in SAR images, numerous other approaches, based on complex mathematical models such as Active Contour Model [39; 40], Level Set Functions [41; 42; 43; 44], Snake models [45] have been tested with good results over large areas. In the last few years, the very fine spatial and radiometric resolution of COSMO-SkyMed SAR images has encouraged the development of several procedures aimed at shoreline extraction. CSK amplitude SAR image processing [46; 47] has provided very satisfactory results in coastline mapping achieving an accuracy equivalent to the spatial resolution, proved by comparing results with in situ shoreline measures acquired during satellite observation. A Bayesian estimation theory able to detect sea boundaries at full resolution and low error rate in a totally unsupervised way has also been proposed [48]. An interferometric technique for the coastline extraction from CSK interferometric pairs, using the coherence loss of pixels belonging to the sea surface, has been developed [38]. Dual polarimetric CSK data for coastline extraction have also been experimented [49; 50; 51], exploiting the correlation between co- and cross-polarized amplitude channels. Furthermore, an innovative coastline extraction procedure from Full-Polarized SAR imagery has been developed to integrate an Autoassociative Neural Network (AANN) and a Pulse-Coupled Neural Network (PCNN) [52]. Field data are always essential to improve the robustness, accuracy and reliability of any approach for coastal risk assessment based on Earth Observation (EO) data. In particular, remotely sensed data must be accurately geocoded, validated and corrected on the basis of the actual marine weather conditions. This paper presents a semi-automatic integrated approach for shoreline detection and monitoring, as well as its application on two different test sites in Apulia Region (South Italy). An innovative model combining backscatter intensity data with the interferometric analysis has been tested on CSK Stripmap HIMAGE mode data providing an accurate shoreline detection and a coastal type classification. Thanks to the integration of satellite SAR images and ground truths (that are used for proper EO data selection, tidal corrections and validation of EO products), the developed processing chain provides a complete SAR image analysis system for shoreline monitoring, allowing to overcome some issues in the processing of SAR data and ensures the best possible results in coastal monitoring, as remarked in the following sections. The paper is structured as follows: Section 2 provides details on the developed integrated EO system and its outlines; Section 3 illustrates the features of the case study and available data and in Section 4 the obtained results are presented. Section 5 contains the discussion on the proposed scheme and in Section 6, conclusions are provided. This paper is an extended version of \"Remote Sensed and In Situ Data: an integrated approach for Coastal Risk Assessment\" published in \"2018 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea) Conference\" Proceedings [53].