Title :
Remote sensing image classification based on multiple classifiers fusion
Author :
Zhao, Quanhua ; Song, Weidong
Author_Institution :
Sch. of Geomatics, Liaoning Tech. Univ., Fuxin, China
Abstract :
There are many methods for Remote Sensing (RS) image classification at present. Different classifiers can obtain diverse accuracies for different RS images or different feature types. Now, the research on RS image classification is focusing on developing new classifiers. Little research has been conducted on making full use of the complementation of different classifiers which may obtain more precise result than single classifier. In the paper, a weighted multiple classifiers fusion method on abstract level was proposed. Firstly, five classifiers were selected to classify one TM image. Then the classification experiment based on weighted multiple classifiers fusion on abstract level and on measurement level was finished. At last, the comparison of classification precision of single classifiers and fusion classifiers was done. Test result proved that the classification accuracy based on fused classifier is higher than single classifier obviously.
Keywords :
image classification; image fusion; remote sensing; RS image classification; abstract level; remote sensing image classification; weighted multiple classifiers fusion method; Accuracy; Artificial neural networks; Bayesian methods; Image classification; Pattern recognition; Remote sensing; Support vector machine classification; RS image; accuracy; confusion matrix; multiple classifiers fusion;
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
DOI :
10.1109/CISP.2010.5647897