ON NEURAL NETWORKS ALGORITHMS FOR OIL SPILL DETECTION WHEN APPLIED TO C- AND X-BAND SAR

被引：0

作者：

Del Frate, F. ^{[1
]}

Latini, D. ^{[1
]}

Scappiti, V. ^{[1
]}

机构：

[1] Univ Roma Tor Vergata, Earth Observat Lab, Via Politecn 1, Rome, Italy

来源：

2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年

关键词：

SAR features; Neural Networks; COSMO-SkyMed; Radarsat; Oil spill;

D O I：

暂无

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The aim of this paper is to introduce new algorithms for the oil spill detection taking fully advantage of the polarimetric and textural features contained in new generation SAR data such as those provided by Radarsat-2 and COSMO-SkyMed missions. The SAR information is exploited using a new statistical decomposition method based on AANN. Thanks to the AANN the original image is represented in terms of Nonlinear principal components (NLPC). The oil spill detection procedure is then directly applied to the new generated components

引用

页码：5249 / 5251

页数：3

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