Paper Info
Title
Unlabeled Sample Reduction in Semi-supervised Graph-Based Band Selection for Hyperspectral Image Classification
Abstract
Semi-supervised graph-based band selection methods have shown satisfying performances to choose the valuable bands for the hyper spectral data classification in case of very limited labeled samples. However, the calculation of adjacency matrices based on all labeled and unlabeled samples requires a large computational load which can be unacceptable with the huge amounts of unlabeled samples available. To address the problem, an unlabeled sample reduction method is proposed. The method involves dimensional reduction through PCA, over-segmentation through watershed, random sample selection from the resulting clusters. The band selection and classification experiments on hyper spectral data demonstrate that the proposed method can help improve the computational efficiency and performances of the graph-based algorithms by choosing the representative samples.
Year
DOI
Venue
2013
10.1109/ICIG.2013.88
ICIG
Keywords
Field
DocType
semi-supervised graph-based band selection,dimensional reduction,computational efficiency,classification experiment,hyperspectral image,unlabeled sample,random sample selection,unlabeled sample reduction,graph-based algorithm,band selection,unlabeled sample reduction method,accuracy,image classification,pca,semi supervised learning,principal component analysis,sampling methods,watershed segmentation,image segmentation,watershed,hyperspectral imaging,adjacency matrices
Adjacency matrix,Computer vision,Graph,Semi-supervised learning,Pattern recognition,Computer science,Image segmentation,Artificial intelligence,Sampling (statistics),Dimensional reduction,Contextual image classification,Principal component analysis
Conference
Citations 
PageRank 
References 
2
0.36
3
Authors
3
Name
Order
Citations
PageRank
Rui Huang1117983.33
Lisha Yang220.70
Zhiqiang Lv32611.28