Please use this identifier to cite or link to this item:
Title: Neural-fuzzy classification for segmentation of remotely sensed images
Authors: 國立臺灣師範大學資訊教育研究所
Chen, Sei-Wang
Chen, Chi-Farn
Chen, Meng-Seng
Cherng, Shen
Fang, Chiung-Yao
Chang, Kuo-En
Issue Date: 1-Nov-1997
Publisher: Institute of Electrical and Electronics Engineers
Abstract: 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 for synthetic images. We also feel comfortable with the results of the real images because their spectral variances are even smaller than the spectral variances of the synthetic images examined.
ISSN: 1053-587X
Other Identifiers: ntnulib_tp_A0904_01_024
Appears in Collections:教師著作

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.