Multicriteria classification method for dimensionality reduction adapted to hyperspectral images

Mahdi Khoder; Serge Kashana; Jihan Khoder; Rafic Younes

doi:10.1117/1.JRS.11.025001

6 April 2017 Multicriteria classification method for dimensionality reduction adapted to hyperspectral images

Mahdi Khoder, Serge Kashana, Jihan Khoder, Rafic Younes

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 025001 (April 2017). https://doi.org/10.1117/1.JRS.11.025001

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.

Citation Download Citation

Mahdi Khoder, Serge Kashana, Jihan Khoder, and Rafic Younes "Multicriteria classification method for dimensionality reduction adapted to hyperspectral images," Journal of Applied Remote Sensing 11(2), 025001 (6 April 2017). https://doi.org/10.1117/1.JRS.11.025001

Received: 13 November 2016; Accepted: 9 March 2017; Published: 6 April 2017

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