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An Adaptive Supervised Nonlinear Feature Extraction for Hyperspectral Imagery Classification

  • Haimiao Ge
  • Liguo Wang
  • Cheng Li
  • Yanzhong Liu
  • Ruixin Chen
Research Article

Abstract

In this paper, an improved version of locally linear Embedding is proposed. In the proposed method, spectral correlation angle is invited to describe the distance between data points, which is expected to fit the hyperspectral image (HSI). The neighborhood graph of the data points is constructed based on supervised method. Different from traditional supervised feature extraction methods, the weight factors, which are used to control the transform, are adaptively achieved. In this way, the input arguments of original algorithm are not increased. To justify the effectiveness of the proposed method, experiments are conducted on two HSIs. Results show that the proposed method can improve the separability of HSI especially in low dimensions.

Keywords

Manifold learning Feature extraction Locally linear embedding (LLE) Hyperspectral image (HSI) 

Notes

Acknowledgements

The work is supported by the National Natural Science Foundation of China (Grant No. 61675051), the Program for Young Teachers Scientific Research in Qiqihar University (Grant No. 2014 k-M07).

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Copyright information

© Indian Society of Remote Sensing 2017

Authors and Affiliations

  • Haimiao Ge
    • 2
  • Liguo Wang
    • 1
  • Cheng Li
    • 2
  • Yanzhong Liu
    • 2
  • Ruixin Chen
    • 3
  1. 1.College of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina
  2. 2.College of Computer and Control EngineeringQiqihar UniversityQiqiharChina
  3. 3.Software DepartmentDaQing ENCH Innovation Technology CompanyDaqingChina

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