SAR Image Feature Extraction and Classification With Fractal-based Description

机译：基于分形描述的SAR图像特征提取与分类

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摘要

In the field of Synthetic Aperture Radar (SAR) image analysis, an effective feature extraction method based on Fractal Brown Increment Random Field (FBRJR) is introduced, and an effective classification method with neuron network based on Adaptive Resonance Theory (ART) is designed accordingly. At last the validity of this systematic approach is tested and compared using the real data of Ku band SAR images.

机译：在合成孔径雷达（SAR）图像分析领域，介绍了一种基于分形布朗增量随机场（FBRJR）的有效特征提取方法，并据此设计了一种基于自适应共振理论（ART）的神经网络有效分类方法。。最后，使用Ku波段SAR图像的真实数据测试并比较了这种系统方法的有效性。

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来源
《2002 6th International Conference on Signal Processing Proceedings (ICSP'02) Vol.2; Aug 26-30, 2002; Beijing, China》|2002年|p.1392-1395|共4页
会议地点 Beijing(CN);Beijing(CN)
作者
Jia Xu; Ling Lu; Zhenming Feng; Yingning Peng;
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作者单位

Electronic Engineering Department, Tsinghua University, Beijing, China, 100084;

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原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
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4. SAR image feature extraction and classification with fractal-based description [C] . Jia Xu, Ling Lu, Zhenming Feng, . 2002

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5. Feature extraction from the image of straight-edge objects and dynamic image/feature classification using non-iterative neural networks. [D] . Chanekasit, Sirikanlaya. 2004

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6. Oil Spill Detection by SAR Images: Dark Formation Detection Feature Extraction and Classification Algorithms [O] . Konstantinos N. Topouzelis 2008

机译：SAR图像的漏油检测：暗层检测特征提取和分类算法
7. Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms [O] . Konstantinos N. Topouzelis 2008

机译：SAR图像的漏油检测：暗层检测，特征提取和分类算法

SAR Image Feature Extraction and Classification With Fractal-based Description

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