Historically, the extraction of coastal line has been performed exploiting optical images, but in the last two decades, some approaches working with synthetic aperture radar (SAR) data have been proposed. Recently, these approaches have been gaining interest due to the availability of high-resolution SAR images. In this letter, a technique for coastal line retrieval from multichannel SAR images is presented. The detection problem is faced in the statistical estimation framework, in particular, exploiting Bayesian estimation theory. The proposed technique is able to detect sea boundaries at full resolution and low error rate in a totally unsupervised way. The performance of the method has been tested using high-resolution COSMO-SkyMed data sets acquired on the Bay of Naples, showing the high accuracy of the proposed technique.
Unsupervised Coastal Line Extraction From SAR Images
BASELICE, FABIO;FERRAIOLI, GIAMPAOLO
2013-01-01
Abstract
Historically, the extraction of coastal line has been performed exploiting optical images, but in the last two decades, some approaches working with synthetic aperture radar (SAR) data have been proposed. Recently, these approaches have been gaining interest due to the availability of high-resolution SAR images. In this letter, a technique for coastal line retrieval from multichannel SAR images is presented. The detection problem is faced in the statistical estimation framework, in particular, exploiting Bayesian estimation theory. The proposed technique is able to detect sea boundaries at full resolution and low error rate in a totally unsupervised way. The performance of the method has been tested using high-resolution COSMO-SkyMed data sets acquired on the Bay of Naples, showing the high accuracy of the proposed technique.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
