Acta Geodaetica et Cartographica Sinica >

2015 , Vol. 44 >Issue 8: 919 - 926

DOI: https://doi.org/10.11947/j.AGCS.2015.20140221

Semi-supervised Graph-based Hyperspectral Image Classification with Active Learning

  • TIAN Yanping ,
  • TAO Chao ,
  • ZOU Zhengrong ,
  • YANG Zhaoxia ,
  • HE Xiaofei
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  • School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Received date: 2014-04-21

  Revised date: 2014-10-21

  Online published: 2015-09-02

Supported by

The National Basic Research Program of China(973 Program)(No.2012CB719903);The National Natural Science Foundation of China(No.41301453);China Postdoctoral Science Foundation(No.2013M530361);Specialized Research Foud for the Doctoral Program of Higher Education(No.20130162120027)

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Abstract

Currently, it is difficult and time-consuming to obtain enough labeled samples for hyperspectral image(HSI) classification, while numerous unlabeled samples can be easily identified but unused. Here, in order to overcome these shortcomings, we proposed a semi-supervised graph-based combined with active learning mechanism approach in this paper. Firstly, we extracted the spatio-spectral feature by reorganizing the spectrum of a pixel with its neighbors, followed by a sorting scheme to make the feature representation to be rotation invariant. Then, the most uncertain samples(namely largest ambiguity samples for classifier) were selected for operator to label with active learning algorithm. Finally, both labeled samples and unlabeled samples were used for semi-supervised classification. The proposed algorithm could guarantee that boundary samples were selected, which would help construct the boundary of a classifier. Simultaneously, even when less labeled samples were available, the proposed method achieved a good classification result by introducing a large number of unlabeled samples. The experimental results on three real hyperspectral images confirmed that the proposed method can obtain higher classification accuracy.

Key words： hyperspectral image classification; semi-supervised graph-based learning; active learning; spatio-spectral feature

Cite this article

TIAN Yanping , TAO Chao , ZOU Zhengrong , YANG Zhaoxia , HE Xiaofei . Semi-supervised Graph-based Hyperspectral Image Classification with Active Learning[J]. Acta Geodaetica et Cartographica Sinica, 2015 , 44(8) : 919 -926 . DOI: 10.11947/j.AGCS.2015.20140221

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