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Computer Science > Computer Vision and Pattern Recognition

arXiv:2210.16239 (cs)
[Submitted on 25 Oct 2022]

Title:Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information

Authors:Hasna Nhaila, Maria Merzouqi, Elkebir Sarhrouni, Ahmed Hammouch
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Abstract:The Hyperspectral image (HSI) contains several hundred bands of the same region called the Ground Truth (GT). The bands are taken in juxtaposed frequencies, but some of them are noisily measured or contain no information. For the classification, the selection of bands, affects significantly the results of classification, in fact, using a subset of relevant bands, these results can be better than those obtained using all bands, from which the need to reduce the dimensionality of the HSI. In this paper, a categorization of dimensionality reduction methods, according to the generation process, is presented. Furthermore, we reproduce an algorithm based on mutual information (MI) to reduce dimensionality by features selection and we introduce an algorithm using mutual information and homogeneity. The two schemas are a filter strategy. Finally, to validate this, we consider the case study AVIRIS HSI 92AV3C.
Keywords: Hyperspectrale images; classification; features selection; mutual information; homogeneity
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2210.16239 [cs.CV]
  (or arXiv:2210.16239v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2210.16239
Journal reference: 2015 Intelligent Systems and Computer Vision, ISCV 2015, 2015, 7106167 - http://www.scopus.com/inward/record.url?eid=2-s2.0-84934343941&partnerID=MN8TOARS
Related DOI: https://doi.org/10.1109/ISACV.2015.7106167

Submission history

From: ELkebir Sarhrouni [view email]
[v1] Tue, 25 Oct 2022 23:55:04 UTC (647 KB)
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