Neural-fuzzy classification for segmentation of remotely sensed images

Sei Wang Chen*, Chi Farn Chen, Meng Seng Chen, Shen Cherng, Chiung Yao Fang, Kuo En Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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 those of synthetic ones examined.

Original languageEnglish
Pages (from-to)2639-2654
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number11
Publication statusPublished - 1997


  • ART neural classifier
  • Adaptive representation
  • Fuzzy clustering algorithm
  • Histogram-based nonuniform coarse coding
  • Measure of performance

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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