Multi-Pol Sar Data Fusion for Coastline Extraction by Neural Networks Chaining

Abstract

In this work we present a new coastline extraction approach, which seeks to enhance performances and to provide automation in shoreline generation with SAR (Synthetic-aperture radar) data. Our approach is designed to harness Multi-Pol SAR acquisitions, while single-pol acquisitions are used in most of the approaches in this area, employing an Autoassociative Neural Network (AANN) for data fusion purposes and a Pulse-Coupled Neural Network (PCNN) for the generation of a final coastline. Using RADARSAT-2 data, main findings are shown, exhibiting better and comparable results with respect to consolidated approaches and with a recent automated method which may be regarded as within the state-of-the art methods in the field of coastline extraction from SAR data.