Paper Info
Title
Clustering-Based Extraction of Near Border Data Samples for Remote Sensing Image Classification.
Abstract
The definition of valuable training samples and automatic classification of land cover with remote sensing data are both classical problems, which are known to be difficult and have attracted major research efforts. In this paper, a method of modified K-means-based support vector machine (SVM) classification is proposed to use a hybrid sample selection that leverages the informativeness and representativeness of training samples to classify real multi/hyperspectral images. The hybrid sample selection (close-to-cluster-border sampling and near-cluster-center sampling) is constructed on the reduced convex hulls (RCHs) of clustering structure and can reduce the risk of overtraining caused by active sample selection of active learning methods. Numerical results obtained on the classification of three challenging remote sensing images (Landsat-7 ETM+, AVIRIS Indian pines, and KSC) by comparing the proposed technique with random sampling (RS) and margin sampling (MS) demonstrate the good efficiency and high accuracy of our approach. © 2012 Springer Science+Business Media, LLC.
Year
DOI
Venue
2013
10.1007/s12559-012-9147-2
Cognitive Computation
Keywords
Field
DocType
K,-means,Support vector machines (SVMs),Close-to-cluster-border sampling,Near-cluster-center sampling,Reduced convex hulls (RCHs),Spectral angular mapper (SAM),Remote sensing images
Data mining,Computer science,Remote sensing,Artificial intelligence,Contextual image classification,Cluster analysis,Land cover,k-means clustering,Pattern recognition,Support vector machine,Hyperspectral imaging,Sampling (statistics),Sample selection,Machine learning
Journal
Volume
Issue
ISSN
5
1
18669964
Citations 
PageRank 
References 
6
0.43
21
Authors
5
Name
Order
Citations
PageRank
Xiaoyong Bian1181.59
Tianxu Zhang220623.18
Xiaolong Zhang360.43
Luxin Yan412316.97
Bo Li5971111.71