Paper Info
Title
A Composite Semisupervised SVM for Classification of Hyperspectral Images
Abstract
This letter presents a novel composite semisupervised support vector machine (SVM) for the spectral-spatial classification of hyperspectral images. In particular, the proposed technique exploits the following: 1) unlabeled data for increasing the reliability of the training phase when few training samples are available and 2) composite kernel functions for simultaneously taking into account spectral and spatial information included in the considered image. Experiments carried out on a hyperspectral image pointed out the effectiveness of the presented technique, which resulted in a significant increase of the classification accuracy with respect to both supervised SVMs and progressive semisupervised SVMs with single kernels, as well as supervised SVMs with composite kernels.
Year
DOI
Venue
2009
10.1109/LGRS.2008.2009324
IEEE Geoscience and Remote Sensing Letters
Keywords
Field
DocType
geophysical techniques,geophysics computing,image classification,support vector machines,composite kernel functions,composite semisupervised support vector machine,hyperspectral images,single kernels,spectral-spatial classification,supervised svm,unlabeled data,composite kernels,kernel methods,remote-sensing hyperspectral image classification,semisupervised classification,support vector machines (svms),computer science education,hyperspectral sensors,remote sensing,kernel method,spatial information,hyperspectral imaging,kernel function,support vector machine,training data,kernel
Kernel (linear algebra),Spatial analysis,Pattern recognition,Computer science,Support vector machine,Composite number,Hyperspectral imaging,Artificial intelligence,Contextual image classification,Kernel method,Composite kernel,Machine learning
Journal
Volume
Issue
ISSN
6
2
1545-598X
Citations 
PageRank 
References 
43
1.90
10
Authors
3
Name
Order
Citations
PageRank
Mattia Marconcini164238.24
Camps-Valls, G.244129.69
Lorenzo Bruzzone34952387.72