Neural-fuzzy classification for segmentation of remotely sensed images

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题名:	Neural-fuzzy classification for segmentation of remotely sensed images
作者:	Chen,SW;Chen,CF;Chen,MS;Cherng,S;Fang,CY;Chang,KE
贡献者:	太空及遙測研究中心
关键词:	SENSING DATA;NETWORK;VALIDITY
日期:	1997
上传时间:	2010-06-29 18:52:08 (UTC+8)
出版者:	中央大學
摘要:	An unsupervised classification technique conceptualized in terms of neural and fuzzy disciplines for the segmentation of remotely sensed images is presented, The process consists of three major steps: 1) pattern transformation; 2) neural classification; 3) fuzzy grouping. In the first step, the multispectral patterns of image pixels are transformed into what we call coarse patterns, In the second step, a delicate classification of pixels is attained by applying an ART neural classifier to the transformed pixel patterns. Since the resultant clusters of pixels are usually too keen to be of practical significance, in the third step, a fuzzy clustering algorithm is invoked to integrate pixel clusters. A function for measuring clustering validity is defined with which the optimal number of classes can be automatically determined by the clustering algorithm, The proposed technique is applied to both synthetic and real images. High classification rates have been achieved far synthetic images. We also feel comfortable with the results of the real images because their spectral variances are even smaller than those of synthetic ones examined.
關聯:	IEEE TRANSACTIONS ON SIGNAL PROCESSING
显示于类别:	[太空及遙測研究中心] 期刊論文

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