PCNN for automatic segmentation and information extraction from X-band SAR imagery

被引：14

作者：

Del Frate, Fabio ^{[1
]}

Latini, Daniele ^{[1
]}

Pratola, Chiara ^{[1
]}

Palazzo, Francesco ^{[2
]}

机构：

[1] Tor Vergata Univ, Dipartimento Informat Sistemi & Prod, Via Politecn, I-100133 Rome, Italy

[2] ESA ESRIN, SERCO Spa, Frascati, Italy

来源：

INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION | 2013年 / 4卷 / 01期

关键词：

pulse-coupled neural networks; segmentation; building detection; oil spill detection; coastline extraction;

D O I：

10.1080/19479832.2012.713398

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

The extremely high number of synthetic aperture radar (SAR) images provided by the current spaceborne missions demand for the development of even more effective automatic techniques for data processing. In this context, neural approaches can give significant contributions being characterised by a high level of automatism. In particular, rather interesting potential is provided by the pulse-coupled neural networks (PCNNs), which have been designed with the idea of simulating the visual cortex of small mammals. In this article, the performance of PCNNs for automatic object extraction from satellite with very high-resolution SAR images is examined by applying them to different cases of interest.

引用

页码：75 / 88

页数：14

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