Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods
 
Creator Murinto, Murinto
Puji Astuti, Nur Rochmah Dyah
Mardhia, Murein Miksa
 
Subject Darwinian Particle Swarm Optimization; FODPSO ;Hypersectral Image ;Multilevel Thresholding; Particle Swarm Optimiziation
 
Description High dimensional problems are often encountered in studies related to hyperspectral data. One of the challenges that arise is how to find representations that are accurate so that important structures can be clearly easily. This study aims to process segmentation of hyperspectral image by using swarm optimization techniques. This experiments use Aviris Indian Pines hyperspectral image dataset that consist of 103 bands. The method used for segmentation image is particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO) and fractional order Darwinian particle swarm optimization (FODPSO). Before process segmentation image, the dimension of the hyperspectral image data set are first reduced by using independent component analysis (ICA) technique to get first independent component. The experimental show that FODPSO method is better than PSO and DPSO, in terms of the average CPU processing time and best fitness value. The PSNR and SSIM values when using FODPSO are better than the other two swarm optimization method. It can be concluded that FODPSO method is better in order to obtain better segmentation results compared to the previous method.
 
Publisher Universitas Ahmad Dahlan
 
Contributor Universitas Ahmad Dahlan
 
Date 2019-03-26
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/311
10.26555/ijain.v5i1.311
 
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 1 (2019): March 2019; 66-75
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/311/ijain_v5i1_p66-75
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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