Segmentasi Citra Remote Sensing Laut Dengan Metode Clustering DBSCAN
Abstract
Kebutuhan terhadap citra jarak jauh di bidang kelautan ini sangat diperlukan terutama dalam bidang pemetaan wilayah zona kedalaman laut ataupun bidang keteknikan yang lain. Oleh karena itu dibutuhkan sistem yang dapat menetukan wilayah zona kedalaman laut melalui citra digital berupa citra penginderaan jauh (remote sensing) yang tentunya diharapkan lebih efisien dari cara konvensional yang sudah ada untuk bisa memetakan zona wilayah kelautan karena hanya membutuhkan sebuah citra input dengan objek laut dari penginderaan jarak jauh. Penelitian ini melakukan identifikasi zona kedalaman laut dari citra menggunakan metode DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Metode DBSCAN diberikan proses pra pengolahan citra baik itu penyesuaian ukuran citra dan kuantisasi warna citra sebelum dilakukan proses DBSCAN agar proses metode DBSCAN bisa bekerja lebih efisien. Proses DBSCAN nantinya akan menghasilkan zona kedalaman laut yang sesuai dengan citra yang ingin diketahui tanpa harus menentukan jumlah cluster terlebih dahulu atau zona kedalaman laut yang ada pada citra yang diujikan. Tingkat keberhasilan metode DBSCAN yang diberikan proses pra pengolahan berupa kuantisasi warna ini berhasil memberikan informasi mengenai zona kedalaman laut sesuai dengan citra ocean yang ingin diketahui dengan rata-rata persentase keberhasilan sebesar 96,3 persen dari beberapa percobaan citra input yang telah dilakukan pada penelitian ini.Downloads
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References
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[3] Xu L, Jackowski M., Goshtasby A, “Segmentation of Skin Cancer Images”, USA, 1999.
[4] J.W. Hedgpeth,”Teratise of Marine ecology and Paleoecology”.New York; Waverly press, 1957
[5] Dasu T, Johnson T, “Exploratory Data Mining and Data Cleaning” Wiley Pub. Inc., Indianapolis: Indiana, 2003.
[6] Han, Jiawei & Kamber, Micheline, “Data Mining – Consepts and Techniques”, Simon Fraser University, USA: Morgan Kaufmann, 2001.
[7] Ye Qixiang, Gao Wen, Zeng Wei, “Color Image Segmentation Using Density-Based Clustering”, IEEE-ICASSP, 2003, 345-348.
[8] Sander J, Ester M, Kriegel H P, Xu X, “Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, in: Data Mining and Knowledge Discovery”, an Int. Journal, Kluwer Academic Publishers, Vol. 2, No. 2, 1998, pp. 169-194. Haixun Wang, Carlo.
[9] Ester M, Kriegel H.-P, Sander J. and Xu X., “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”. Proc. 2nd Int. Conf. On Knowledge Discovery and Data Mining. Portland, OR, 1996, 226-231.
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[11] Gonzalez Rafael, Wintz Paul, “Digital Image Processing”. Addison-Wesley Publishing Company, Inc., 1999.
[12] Ocean Data View Home, Http://odv.awi.de/ diakses tanggal 1 mei 2012
[13] Moreira Adriano, Santos Maribel Y and Carneiro Sofia, “Density-based clustering algorithms – DBSCAN and SNN”, University of Minho – Portugal, 2005.
How to Cite
SUWIJA PUTRA, I Made.
Segmentasi Citra Remote Sensing Laut Dengan Metode Clustering DBSCAN.
Majalah Ilmiah Teknologi Elektro, [S.l.], v. 12, n. 2, dec. 2013.
ISSN 2503-2372.
Available at: <https://ojs.unud.ac.id/index.php/mite/article/view/15548>. Date accessed: 19 nov. 2024.
Section
Articles
Keywords
DBSCAN, laut, segmentasi, kuantisasi warna, cluster
This work is licensed under a Creative Commons Attribution 4.0 International License
