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A method of band selection of remote sensing image based on clustering and intra-class index

  • 1200: Machine Vision Theory and Applications for Cyber Physical Systems
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Abstract

In order to avoid the problem of over-dependent on the similarity to select the band but ignoring the amount of information and the correlation of the band, this paper proposes a method of hyperspectral image feature perception based on clustering and intra-class frequency band index. In this paper, the band texture feature vectors are used to calculate the similarity matrix. Then affine propagation clustering algorithm clusters all the bands according to the similarity matrix. The band with the largest intra-class band index in a certain cluster is selected as the representative band, so as to achieve the purpose of band selection. Finally, support vector machine classification algorithm is used to classify the objects on the image after band selection. By combining the Affine Propagation algorithm and Intra-Class Band Index, this paper proposed the AP-ICBI algorithm so that the band with large amount of information and small correlation can be selected in the high-quality band clustering results. In the experiment, the overall classification accuracy (OA), the Kappa coefficient and user accuracy (UA) are taken as the evaluation indexes. The experimental results showed that the proposed AP-ICBI algorithm can effectively improve the classification accuracy comparing with other methods.

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Authors and Affiliations

  1. Xidian University School of Aerospace Science and Technology, Xian, Shaanxi, China

    Yunyi Yan, Wenyi Yu & Lingxia Zhang

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  1. Yunyi Yan
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  2. Wenyi Yu
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  3. Lingxia Zhang
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Correspondence to Lingxia Zhang.

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Yan, Y., Yu, W. & Zhang, L. A method of band selection of remote sensing image based on clustering and intra-class index. Multimed Tools Appl 81, 22111–22128 (2022). https://doi.org/10.1007/s11042-021-11865-1

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  • DOI: https://doi.org/10.1007/s11042-021-11865-1

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