DocumentCode :
1957138
Title :
Knowledge discovery from multispectral satellite images
Author :
McCaslin, Sara ; Kulkarni, Arun
Author_Institution :
Dept. of Comput. Sci., Texas Univ., Tyler, TX, USA
fYear :
2002
fDate :
2002
Firstpage :
249
Lastpage :
254
Abstract :
In this paper, we propose a method to extract and reduce fuzzy IF-THEN rules from fuzzy neural systems. After training, fuzzy rules are extracted from the fuzzy neural network by backtracking along the weighted paths through the neural network. These rules are then reduced by use of a fuzzy associative memory (FAM) bank. We used this algorithm to extract classification rules from a multi-spectral satellite image. The image represents the Mississippi river bottomland. In order to verify the rule extraction method, measures such as accuracy, overall Kappa and fidelity are used. The results are presented in the paper.
Keywords :
backtracking; content-addressable storage; data mining; fuzzy neural nets; image classification; image colour analysis; remote sensing; rivers; Mississippi river bottomland; accuracy; classification rules; fidelity; fuzzy IF-THEN rules; fuzzy associative memory bank; fuzzy neural network; fuzzy rule extraction; knowledge discovery; multi-spectral satellite images; neural system training; overall Kappa; weighted path backtracking; Associative memory; Computer science; Data mining; Data visualization; Decoding; Fuzzy neural networks; Fuzzy systems; Neural networks; Rivers; Satellites;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American
Print_ISBN :
0-7803-7461-4
Type :
conf
DOI :
10.1109/NAFIPS.2002.1018064
Filename :
1018064
Link To Document :
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