An Algorithm and Heuristic based on Normalized Mutual Information for
Dimensionality Reduction and Classification of Hyperspectral images
- URL: http://arxiv.org/abs/2210.13456v1
- Date: Sat, 22 Oct 2022 02:58:04 GMT
- Title: An Algorithm and Heuristic based on Normalized Mutual Information for
Dimensionality Reduction and Classification of Hyperspectral images
- Authors: Elkebir Sarhrouni, Ahmed Hammouch and Driss Aboutajdine
- Abstract summary: Hyperspectral image (HSI) is a set of more than a hundred bidirectional measures (called bands) of the same region (called ground truth map: GT)
We introduce an algorithm based on Normalized Mutual Information to select relevant and no redundant bands, necessary to increase classification accuracy of HSI.
- Score: 0.4297070083645048
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In the feature classification domain, the choice of data affects widely the
results. The Hyperspectral image (HSI), is a set of more than a hundred
bidirectional measures (called bands), of the same region (called ground truth
map: GT). The HSI is modelized at a set of N vectors. So we have N features (or
attributes) expressing N vectors of measures for C substances (called classes).
The problematic is that it's pratically impossible to investgate all possible
subsets. So we must find K vectors among N, such as relevant and no redundant
ones; in order to classify substances. Here we introduce an algorithm based on
Normalized Mutual Information to select relevant and no redundant bands,
necessary to increase classification accuracy of HSI.
Keywords: Feature Selection, Normalized Mutual information, Hyperspectral
images, Classification, Redundancy.
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