Image and Signal Processing Methods

Multicriteria classification method for dimensionality reduction adapted to hyperspectral images

[+] Author Affiliations
Mahdi Khoder, Serge Kashana, Rafic Younes

Lebanese University, Faculty of Engineering, Tripoli, Lebanon

Jihan Khoder

University of Versailles Saint-Quentin en-Yvelines, LISV Laboratory, Paris, France

J. Appl. Remote Sens. 11(2), 025001 (Apr 06, 2017). doi:10.1117/1.JRS.11.025001
History: Received November 13, 2016; Accepted March 9, 2017
Text Size: A A A

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.

© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Mahdi Khoder ; Serge Kashana ; Jihan Khoder and Rafic Younes
"Multicriteria classification method for dimensionality reduction adapted to hyperspectral images", J. Appl. Remote Sens. 11(2), 025001 (Apr 06, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.025001


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.