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

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10116))

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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.

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Notes

  1. 1.

    The overall accuracy is defined as the ratio of correctly classified samples to all test samples, the average accuracy is calculated by simply averaging the accuracies for each class, and the \(\kappa \) coefficient is computed based on the confusion matrix of different classes.

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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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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Chunjuan Bo, Dong Wang & Huchuan Lu

  2. College of Electromechanical Engineering, Dalian Nationalities University, Dalian, China

    Chunjuan Bo

Authors
  1. Chunjuan Bo
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  2. Dong Wang
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  3. Huchuan Lu
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Corresponding author

Correspondence to Dong Wang .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Bo, C., Wang, D., Lu, H. (2017). Hyperspectral Image Classification via a Joint Weighted K-Nearest Neighbour Approach. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-54407-6_23

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  • Online ISBN: 978-3-319-54407-6

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