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Modified hierarchical k-nearest neighbor method with application to land-cover classification
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Paper Abstract

In this paper, we propose a land-cover classification method based on a modified hierarchical k-nearest neighbor (MHkNN) algorithm to achieve a high classification accuracy. The proposed method introduces a reliability measure for each training sample, which is defined as confidence in the sample belonging to each of the considered classes. The method performs the majority voting considering not only the number of the training samples, but also their reliabilities. The classification performance of the proposed method is compared to that of the conventional land-cover classification methods. The effectiveness of the proposed method is verified by applying it to real remote sensing images.

Paper Details

Date Published: 22 March 2019
PDF: 4 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493R (22 March 2019); doi: 10.1117/12.2521356
Show Author Affiliations
Tatsuya Hayashi, Kyushu Institute of Technology (Japan)
Hakaru Tamukoh, Kyushu Institute of Technology (Japan)
Ryosuke Kubota, National institute of Technology, Ube College (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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