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Journal of Applied Remote Sensing

Multicriteria classification method for dimensionality reduction adapted to hyperspectral images
Author(s): Mahdi Khoder; Serge Kashana; Jihan Khoder; Rafic Younes
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Paper Abstract

Due to the incredible growth of high dimensional datasets, we address the problem of unsupervised methods sensitive to undergoing different variations, such as noise degradation, and to preserving rare information. Therefore, researchers nowadays are forced to develop techniques to meet the needed requirements. In this work, we introduce a dimensionality reduction method that focuses on the multiobjectives of multiple images taken from multiple frequency bands, which form a hyperspectral image. The multicriteria classification algorithm technique compares and classifies these images based on multiple similarity criteria, which allows the selection of particular images from the whole set of images. The selected images are the ones chosen to represent the original set of data while respecting certain quality thresholds. Knowing that the number of images in a hyperspectral image signifies its dimension, choosing a smaller number of images to represent the data leads to dimensionality reduction. Also, results of tests of the developed algorithm on multiple hyperspectral image samples are shown. A comparative study later on will show the advantages of this technique compared to other common methods used in the field of dimensionality reduction.

Paper Details

Date Published: 6 April 2017
PDF: 30 pages
J. Appl. Rem. Sens. 11(2) 025001 doi: 10.1117/1.JRS.11.025001
Published in: Journal of Applied Remote Sensing Volume 11, Issue 2
Show Author Affiliations
Mahdi Khoder, Lebanese Univ. (Lebanon)
Serge Kashana, Lebanese Univ. (Lebanon)
Jihan Khoder, Univ. de Versailles Saint-Quentin-en Yvelines (France)
Rafic Younes, Lebanese Univ. (Lebanon)


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