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Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification

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Abstract

Hyperspectral image (HSI) classification is a very active research topic in remote sensing and has numerous potential applications. This paper presents a simple but effective classification method based on spectral-spatial information and K-nearest neighbor (KNN). To be specific, we propose a spectral-spatial KNN (SSKNN) method to deal with the HSI classification problem, which effectively exploits the distances all neighboring pixels of a given test pixel and training samples. In the proposed SSKNN framework, a set-to-point distance is exploited based on least squares and a weighted KNN method is used to achieve stable performance. By using two standard HSI benchmark, we evaluate the proposed method by comparing it with eight competing methods. Both qualitative and quantitative results demonstrate our SSKNN method achieves better performance than other ones.

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Notes

  1. AVIRIS: Airborne Visible/Infrared Imaging Spectrometer.

  2. These two public data sets can be downloaded from: www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

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Acknowledgments

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, 116024, China

    Chunjuan Bo, Huchuan Lu & Dong Wang

  2. College of Electromechanical Engineering, Dalian Minzu University, Dalian, 116600, China

    Chunjuan Bo

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  1. Chunjuan Bo
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  2. Huchuan Lu
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  3. Dong Wang
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Correspondence to Dong Wang.

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Bo, C., Lu, H. & Wang, D. Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification. Multimed Tools Appl 77, 10419–10436 (2018). https://doi.org/10.1007/s11042-017-4403-9

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