Acta Geodaetica et Cartographica Sinica >

2017 , Vol. 46 >Issue 9: 1098 - 1106

DOI: https://doi.org/10.11947/j.AGCS.2017.20170121

Semi-supervised Spatial-spectral Discriminant Analysis for Hyperspectral Image Classification

  • HOU Banghuan ,
  • WANG Kun ,
  • YAO Minli ,
  • JIA Weimin ,
  • WANG Rong
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  • 1. Department of Information Engineering, Rocket Force Engineering University, Xi'an 710025, China;
    2. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China

Received date: 2017-03-20

  Revised date: 2017-07-24

  Online published: 2017-10-12

Supported by

The Young Scientists Fund of the National Natural Science Foundation of China (No. 61401471);The China Postdoctoral Science Foundation(No. 2014M562636)

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Abstract

In order to make full use of the spatial information embedded in the hyperspectral image to improve the classification accuracy, a semi-supervised spatial-spectral discriminant analysis (S3DA) algorithm for hyperspectral image classification is proposed. According to the spatial consistency property of hyperspectral image, the intra-class scatter matrix infered from a little labeled samples preserves the spectral similarity of the same class pixels, while the spatial local pixel scatter matrix defined by the unlabeled spatial neighbors uncovers the spatial-domain local pixel neighborhood structures and the ground objects detailed distribution. The S3DA method not only maintains the spectral-domain separability of the data set, but also preserves the spatial-domain local pixel neighborhood structure, which promotes the compactness of the same class pixels or the spatial neighbor pixels in the projected subspace and enhances the classification performance. The overall classification accuracies respectively reach 81.50% and 71.77% on the PaviaU and Indian Pines data sets. Compared with the traditional spectral methods, the proposed method can effectively improve ground objects classification accuracy.

Key words： hyperspectral image classification; feature extraction; discriminant analysis; spatial-spectral; semi-supervised learning; spatial neighbors

Cite this article

HOU Banghuan , WANG Kun , YAO Minli , JIA Weimin , WANG Rong . Semi-supervised Spatial-spectral Discriminant Analysis for Hyperspectral Image Classification[J]. Acta Geodaetica et Cartographica Sinica, 2017 , 46(9) : 1098 -1106 . DOI: 10.11947/j.AGCS.2017.20170121

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