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Proceedings Paper

Automatic procedure for coastline extraction exploiting full-pol SAR imagery and AANN-PCNN processing chain
Author(s): Leonardo De Laurentiis; Daniele Latini; Fabio Del Frate; Giovanni Schiavon
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Paper Abstract

Coastal environment is worldwide recognized as an important asset for mankind. Relevant threats, such as erosion and changes in the territory caused by anthropogenic activities, should be addressed appropriately to support authorities and environmental organizations. Coastline extraction procedure is a fundamental task in relation to the monitoring of coastal surroundings, public security and study on potential climate change effects. In this work a new method is proposed, which aims at improving the coastline extraction procedure by harnessing Full-Pol SAR imagery and a processing chain constituted by cascading an Autoassociative Neural Network (AANN) and a Pulse-Coupled Neural Network (PCNN). The AANNs, also known as autoencoders, have been widely used in the literature for nonlinear features extraction and component analysis. This kind of neural network is designed to replicate the input into the output layer. When this task is considered as fulfilled, a good compressed input representation must be present in the bottleneck layer, enabling the extraction of significant features. Conversely the PCNNs don’t need training stages, and are proven effective in the image processing and segmentation tasks. Describing the proposed method in a nutshell, during the first stage the AANN aims at extracting features that would help the land-sea separation process; in the next stage, the PCNN aims at producing the final segmentation and helps to perform the coastline extraction task subsequently executed. Major features of the method mainly consist of the complete processing automation and the novel architecture design which chains different neural networks to accomplish the coastline extraction task.

Paper Details

Date Published: 9 October 2018
PDF: 10 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107890Y (9 October 2018); doi: 10.1117/12.2325582
Show Author Affiliations
Leonardo De Laurentiis, Univ. of Rome Tor Vergata (Italy)
Daniele Latini, Univ. of Rome Tor Vergata (Italy)
Fabio Del Frate, Univ. of Rome Tor Vergata (Italy)
Giovanni Schiavon, Univ. of Rome Tor Vergata (Italy)


Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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