Band selection for hyperspectral image visualization and classification
Submitted: 2024-08-20
|Accepted: 2025-03-15
|Published: 2025-05-20
Copyright (c) 2025 Mery L. Picco, Marcelo S. Ruiz, Juliana R. Maldonado
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
hyperspectral images, band selection, classification, regularization methods
Supporting agencies:
Abstract:
In the hyperspectral remote sensing images processing, band selection is an essential task for many specific applications, including supervised classification. The objective of this work is to compare the performance of the classical strategy, which involves variable selection as a preliminary step to classification, with new proposals of penalized algorithms that perform classification and variable selection simultaneously. For the comparison, an extract of a hyperspectral image EO-1 Hyperion, covering an area in the province of Córdoba, Argentina, was used. Additionally, a simulation study was conducted. The obtained results show that penalized algorithms are more effective in selecting relevant bands while providing good predictive properties, mainly in the context of high dimensionality, that is, when the size of the training sample is small relative to the number of variables.
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