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Hyperspectral Image Classification Using Semi-supervised Random Forest

  • Sunit Kumar AdhikaryEmail author
  • Sourish Gunesh Dhekane
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In this paper, a hyperspectral image classification technique is proposed using semi-supervised random forest (SSRF). Robust node splitting in the random forest requires enormous training data, which is scarce in remote sensing applications. In order to overcome this drawback, we propose utilizing unlabeled data in conjunction with labeled data to assist the splitting process. Moreover, in order to tackle the curse of dimensionality associated with a hyperspectral image, we explore nonnegative matrix factorization (NMF) to remove redundant information. Experimental results confirm the efficacy of the proposed method.

Keywords

Hyperspectral imaging Semi-supervised learning Random forest Nonnegative matrix factorization 

Notes

Acknowledgements

The authors thank Prof. Gamba for providing the University of Pavia dataset.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sunit Kumar Adhikary
    • 1
    Email author
  • Sourish Gunesh Dhekane
    • 1
  1. 1.Indian Institute of Information Technology GuwahatiGuwahatiIndia

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