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Bayesian gravitation based classification for hyperspectral images.

Zhang, Aizhu; Sun, Genyun; Pan, Zhaojie; Ren, Jinchang; Jia, Xiuping; Zhang, Chenglong; Fu, Hang; Yao, Yanjuan

Authors

Aizhu Zhang

Genyun Sun

Zhaojie Pan

Jinchang Ren

Xiuping Jia

Chenglong Zhang

Hang Fu

Yanjuan Yao



Abstract

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples.

Citation

ZHANG, A., SUN, G., PAN, Z., REN, J., JIA, X., ZHANG, C., FU, H. and YAO, Y. 2022. Bayesian gravitation based classification for hyperspectral images. IEEE transactions on geoscience and remote sensing [online], Early Access. Available from: https://doi.org/10.1109/TGRS.2022.3203488

Journal Article Type Article
Acceptance Date Sep 22, 2022
Online Publication Date Sep 22, 2022
Deposit Date Sep 29, 2022
Publicly Available Date Sep 29, 2022
Journal IEEE transactions on geoscience and remote sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher IEEE Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/TGRS.2022.3203488
Keywords Bayes methods; Bayesian theorem; Data models; Gravitation; Gravity; Hyperspectral image; Hyperspectral imaging; Image classification; Kernel; Testing; Training
Public URL https://rgu-repository.worktribe.com/output/1760452

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ZHANG 2022 Bayesian gravitation (AAM v2) (1.8 Mb)
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©2022 IEEE

Version
Updated 2022-10-11





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