Paper Info
Title
Study of Sub-Pixel Classification Algorithms for High Dimensionality Data Set
Abstract
In this work fuzzy set theory based as well as statistical learning algorithm have been studied at sub-pixel classification level. Here two Fuzzy set theory based classifiers, namely, Fuzzy c-Means (FCM) and Possibilistic c- Means (PCM) have been used in supervised modes. Support Vector Machines (SVMs) have been used in this study for density estimation as a statistical learning based sub-pixel classifier while using Mean Field (MF) method for learning. An in-house package SMIC (Sub-Pixel Multi-Spectral Image Classifier) was used and sensitivity of all the three algorithms (FCM, PCM and SVMs) has been checked for dimensionality data sets at 3 to 14 bands from ASTER data. The accuracy of sub-pixel classification outputs has been evaluated using Fuzzy Error Matrix (FERM). In contrast to FCM and PCM, SVM approach showed a clear increase in the accuracy with higher dimensionality data and clearly out performed other two approaches for sub-pixel classification.
Year
DOI
Venue
2006
10.1109/IGARSS.2006.243
Denver, CO
Keywords
Field
DocType
fuzzy logic,geophysical techniques,geophysics computing,image classification,remote sensing,support vector machines,terrain mapping,ASTER data,FCM,FERM,Fuzzy Error Matrix,Fuzzy c-Means,MF method,Mean Field method,PCM,Possibilistic c-Means,SMIC,SVMs,SubPixel MultiSpectral Image Classifier,Support Vector Machines,density estimation,fuzzy set theory,statistical learning algorithm,subpixel classification algorithms,supervised modes
Density estimation,Data mining,Data set,Computer science,Fuzzy set,Artificial intelligence,Classifier (linguistics),Contextual image classification,Pattern recognition,Fuzzy logic,Support vector machine,Algorithm,Curse of dimensionality
Conference
ISSN
ISBN
Citations 
2153-6996
0-7803-9510-7
1
PageRank 
References 
Authors
0.37
6
3
Name
Order
Citations
PageRank
Anil Kumar112.74
V. K. Dadhwal26511.01
Ghosh, S.K.311.05