In this paper, a novel automated coastline extraction method from SAR (Synthetic Aperture Radar) data is presented. The method is designed to exploit radar backscatter coefficients ((Formula presented.)) from multipolarization SAR acquisitions (the 4 classic co- and cross-polarized polarizations), whereas single-pol data are employed in the majority of methods in this field, implementing data fusion through the use of an autoencoder neural network and producing the coastline by harnessing a Pulse-Coupled Neural Network (PCNN). Main results are presented throughout the paper, demonstrating superiority and comparability with established methods and with a recent automated algorithm that can be considered among the state-of-the art techniques in this field; furthermore, effectiveness of data fusion and segmentation obtained through the mentioned neural networks has been compared to that of several combinations of the same networks with different frameworks: a different data fusion framework, obtained through the use of linear Principal Component Analysis (PCA), and a different binarization framework, based on the use of Expectation-Maximization (EM) image segmentation. Main achievements of presented technique consist in enabling a possible faster processing as well as the opportunity of operating with an improved fused information content on coastline, together with very high accuracy results.

De Laurentiis, L., Del Frate, F., Latini, D., Schiavon, G. (2021). SAR data fusion and a novel joint use of neural networks for coastline extraction. INTERNATIONAL JOURNAL OF REMOTE SENSING, 42(22), 8734-8759 [10.1080/01431161.2021.1986237].

SAR data fusion and a novel joint use of neural networks for coastline extraction

Del Frate F.;Latini D.;Schiavon G.
2021-01-01

Abstract

In this paper, a novel automated coastline extraction method from SAR (Synthetic Aperture Radar) data is presented. The method is designed to exploit radar backscatter coefficients ((Formula presented.)) from multipolarization SAR acquisitions (the 4 classic co- and cross-polarized polarizations), whereas single-pol data are employed in the majority of methods in this field, implementing data fusion through the use of an autoencoder neural network and producing the coastline by harnessing a Pulse-Coupled Neural Network (PCNN). Main results are presented throughout the paper, demonstrating superiority and comparability with established methods and with a recent automated algorithm that can be considered among the state-of-the art techniques in this field; furthermore, effectiveness of data fusion and segmentation obtained through the mentioned neural networks has been compared to that of several combinations of the same networks with different frameworks: a different data fusion framework, obtained through the use of linear Principal Component Analysis (PCA), and a different binarization framework, based on the use of Expectation-Maximization (EM) image segmentation. Main achievements of presented technique consist in enabling a possible faster processing as well as the opportunity of operating with an improved fused information content on coastline, together with very high accuracy results.
2021
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/02
English
Con Impact Factor ISI
Data fusion; Data mining; Extraction; Image segmentation; Maximum principle; Neural networks; Principal component analysis; Synthetic aperture radar; Backscatter coefficients; Co-polarized; Coastline extraction; Cross-polarized; Extraction method; Multi-polarization; Neural-networks; Radar backscatter; Radar data; Radar data fusion
De Laurentiis, L., Del Frate, F., Latini, D., Schiavon, G. (2021). SAR data fusion and a novel joint use of neural networks for coastline extraction. INTERNATIONAL JOURNAL OF REMOTE SENSING, 42(22), 8734-8759 [10.1080/01431161.2021.1986237].
De Laurentiis, L; Del Frate, F; Latini, D; Schiavon, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/313347
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