A Hyperspectral Image Classification Method Based on 2-D Compact Variational Mode Decomposition

doi:10.1109/LGRS.2023.3268776

A Hyperspectral Image Classification Method Based on 2-D Compact Variational Mode Decomposition

2023-01-01

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DOI：10.1109/LGRS.2023.3268776

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Supervised hyperspectral classification is one of the important applications in hyperspectral remote sensing. Currently, the enormous complexity of the hyperspectral data makes hyperspectral feature extraction a very challenging job. In this letter, we propose a feature extraction approach by developing a 2-D compact variational mode decomposition (2-D-C-VMD) for the supervised hyperspectral images (HSIs) classification. Specifically, the HSIs are first decomposed into a series of mode components using 2-D-C-VMD. Then for the subsequent supervised classification, a collection of feature samples are created by elaborating the collected pattern components. Finally, a support vector machine (SVM) is adopted as the classifier to validate the aforementioned feature extraction algorithm. According to experimental findings, this approach obtained a high level of classification accuracy with a compact feature extraction process and the time for feature extraction is also shortened.

Keywords:

2-D compact variational mode decomposition (2-D-C-VMD)

feature extraction

supervised hyperspectral image (HSI) classification

A Hyperspectral Image Classification Method Based on 2-D Compact Variational Mode Decomposition

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