A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

被引：0
|
作者：
Fu Yusheng Xie Yan Pi Yiming Hou Yinming College of Electro Eng Univ of Electro Sci And Tech of China Chengdu China [610054 ]
机构：
来源：
JournalofElectronics | 2006年 / 04期
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D O I：
暂无
中图分类号：
TN957.52 [数据、图像处理及录取];
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摘要：
In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.
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页码：598 / 601
页数：4
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