Paper Info
Title
An Application of Neuro-fuzzy System in Remote Sensing Image Classification
Abstract
This paper introduces a classification algorithm-NEFCLASS (neuro-fuzzy classification) to classify remote sensing images landsat7 etm+. The NEFCLASS combines neural networks and fuzzy systems to learn from the training data and generate conditional linguistic rules. Then we use the rules to classify the land-cover/land-use classes in landsat7 etm+ images covering main Bayannaoer city of Inner Mongolia Autonomous Region selected in August 2007. Compared to the ground truth, the experiment result shows that the overall classification accuracy can achieve to 79.93%.
Year
DOI
Venue
2008
10.1109/CSSE.2008.1222
Computer Systems: Science & Engineering
Keywords
Field
DocType
remote sensing image classification,fuzzy system,remote sensing,neuro-fuzzy,images landsat7 etm,ground truth,learning (artificial intelligence),experiment result,remote sensing image,inner mongolia autonomous region,neuro-fuzzy classification,overall classification accuracy,conditional linguistic rules,classification algorithm-nefclass,landsat7 etm,neurofuzzy classification,image classification,neuro-fuzzy system,classification,remote sensing images,fuzzy neural nets,conditional linguistic rule,learning artificial intelligence,fuzzy sets,classification algorithms,accuracy,neuro fuzzy,fuzzy systems,reactive power
Data mining,Computer science,Remote sensing,Fuzzy set,Artificial intelligence,Fuzzy control system,Contextual image classification,Artificial neural network,Training set,Neuro-fuzzy,Pattern recognition,Ground truth,Statistical classification,Machine learning
Conference
Volume
ISBN
Citations 
1
978-0-7695-3336-0
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Wu Wei120414.84
Guanglai Gao27824.57