Computer Science ›› 2018, Vol. 45 ›› Issue (12): 223-228.doi: 10.11896/j.issn.1002-137X.2018.12.037

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Hyperspectral Image Classification Based on Adaptive Active Learning and Joint Bilateral Filtering

LI Chang-li1, ZHANG Lin1, FAN Tang-huai2   

  1. (College of Computer and Information,Hohai University,Nanjing 211100,China)1
    (School of Information Engineering,Nanchang Institute of Technology,Nanchang 330099,China)2
  • Received:2017-11-22 Online:2018-12-15 Published:2019-02-25

Abstract: It is very important to select appropriate samples as training samples to train the classifier in hyperspectral image classification (HIC).In this paper,the uncertainty and representativeness of the sample are combined,and the sample selection is completed by the adaptive active learning method.The Kernel-K clustering means is used to obtain representative samples,while the uncertainty is determined by the weighted sum of the probability difference between the optimal label and the suboptimal label and their ratio.In addition,in order to improve the accuracy of classification,the joint bilateral filtering is used to obtain the spatial information of hyperspectral image,and it is incorporated into the classification process.Finally,a spatial-spectral HIC approach is proposed,which combines adaptive active learning and joint bilateral filtering.The experimental results show the superiority of the proposed method.

Key words: Active learning, Hyperspectral image classification (HIC), Joint bilateral filtering, Spatial-spectral

CLC Number: 

  • TN911.7
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