Particle swarm optimization of kernel-based fuzzy c-mParticle swarm optimization of kernel-based fuzzy c-means for hyperspectral data clustering eans for hyperspectral data clustering

Saeid Niazmardi; Amin Alizadeh Naeini; Saeid Homayouni; Abdolreza Safari; Farhad Samadzadegan

doi:10.1117/1.JRS.6.063601

31 October 2012 Particle swarm optimization of kernel-based fuzzy c-mParticle swarm optimization of kernel-based fuzzy c-means for hyperspectral data clustering eans for hyperspectral data clustering

Saeid Niazmardi, Amin Alizadeh Naeini, Saeid Homayouni, Abdolreza Safari, Farhad Samadzadegan

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 6, Issue 1, 063601 (October 2012). https://doi.org/10.1117/1.JRS.6.063601

Abstract

Hyperspectral data classification using supervised approaches, in general, and the statistical algorithms, in particular, need high quantity and quality training data. However, these limitations, and the high dimensionality of these data, are the most important problems for using the supervised algorithms. As a solution, unsupervised or clustering algorithms can be considered to overcome these problems. One of the emerging clustering algorithms that can be used for this purpose is the kernel-based fuzzy c-means (KFCM), which has been developed by kernelizing the FCM algorithm. Nevertheless, there are some parameters that affect the efficiency of KFCM clustering of hyperspectral data. These parameters include kernel parameters, initial cluster centers, and the number of spectral bands. To address these problems, two new algorithms are developed. In these algorithms, the particle swarm optimization method is employed to optimize the KFCM with respect to these parameters. The first algorithm is designed to optimize the KFCM with respect to kernel parameters and initial cluster centers, while the second one selects the optimum discriminative subset of bands and the former parameters as well. The evaluations of the results of experiments show that the proposed algorithms are more efficient than the standard k-means and FCM algorithms for clustering hyperspectral remotely sensed data.

Citation Download Citation

Saeid Niazmardi, Amin Alizadeh Naeini, Saeid Homayouni, Abdolreza Safari, and Farhad Samadzadegan "Particle swarm optimization of kernel-based fuzzy c-mParticle swarm optimization of kernel-based fuzzy c-means for hyperspectral data clustering eans for hyperspectral data clustering," Journal of Applied Remote Sensing 6(1), 063601 (31 October 2012). https://doi.org/10.1117/1.JRS.6.063601

Published: 31 October 2012

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available