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Hyperspectral Image Classification via a Joint Weighted K-Nearest Neighbour Approach

  • Chunjuan Bo
  • Dong WangEmail author
  • Huchuan Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

In this paper, we propose a simple yet effective classification framework to conduct hyperspectral image (HSI) classification based on K-nearest neighbour (KNN) and joint model. First, we extend the traditional KNN method to deal with the HSI classification problem by introducing its domain knowledge in HSI data. To be specific, we develop a joint KNN approach to solve the HSI classification problem by considering the distances between all neighbouring pixels of a given test pixel and training samples. Second, we exploit a set-to-point distance between neighbouring pixels and each training sample, and introduce this distance into the joint KNN framework. In addition, a weighted KNN method is adopted to achieve stable performance based on our empirical observations. Both qualitative and quantitative results illustrate that our method achieves better performance than other classic and popular methods.

Keywords

Sparse Representation Hyperspectral Image Near Neighbour Probabilistic Graphical Model Basic Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported in part by the Natural Science Foundation of China under Grant No. 61502070, and in part by Fundamental Research Funds for Central Universities under Grant No. DUT16RC(4)16.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina
  2. 2.College of Electromechanical EngineeringDalian Nationalities UniversityDalianChina

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