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All Journal Sainteks Jurnal Informatika Jurnal Sarjana Teknik Informatika International Journal of Advances in Intelligent Informatics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JIKO (Jurnal Informatika dan Komputer) IJID (International Journal on Informatics for Development) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science
Nur Rochmah Dyah Pujiastuti
Universitas Ahmad Dahlan
Arts Humanities Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : International Journal of Advances in Intelligent Informatics

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Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods Murinto Murinto; Nur Rochmah Dyah Puji Astuti; Murein Miksa Mardhia
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.311

Abstract

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.
Co-Authors Abdullah Sayfei Nurdin Ali Tarmuji Anwar, Muhammad Dhabit Armandira Maulana P. Dewi Soyusiawati Edy Nugroho Eka Hidayah Meliana Dewi Eko Aribowo Fiftin Noviyanto Hanif, Ardhan Herman Yuliansyah Herman Yuliansyah, Herman Ika Arfiani M.Asrori Eko Cahyono Maulana P., Armandira Murein Miksa Mardhia Murinto Murinto Nugroho, Edy Nuril Anwar, Nuril Panca Handika Suwardi Sakirno, Muhammad Bayu Aji Sri Winiarti Tri Rezeki Kustiadi Widodo Ardi Saputra Yoga Putra Pamungkas