Uji Akurasi Klasifikasi Terbimbing Berbasis Piksel Pada Citra Sentinel 2-A Menggunakan Citra Tegak Resolusi Tinggi Tahun 2019 di Kota Padang

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Dedy Fitriawan

Abstract

Currently, land cover data sourced from satellite imagery are increasingly being used with advances in sensing technology that are increasingly sophisticated. Maximum likelihood is one of the digital image classification methods that have long been used for pixel-based image classification. This study aims to classify land cover on Sentinel-2A satellite imagery using the maximum likelihood method to see the level of accuracy in that method. The accuracy test is carried out by comparing the results of the classification of the land cover map on the Sentinel-2A image with the sample in the High Resolution Upright Image with the acquisition date which is said to be Worldview and Geoeye imagery in 2019. From the classification results obtained an overall value of 90.81% with the type of land cover highest level of accuracy is wetlands and built-up areas. Meanwhile, the type of cover with the lowest accuracy or the most errors occurred in mixed garden types.

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How to Cite
FITRIAWAN, Dedy. Uji Akurasi Klasifikasi Terbimbing Berbasis Piksel Pada Citra Sentinel 2-A Menggunakan Citra Tegak Resolusi Tinggi Tahun 2019 di Kota Padang. Jurnal Azimut, [S.l.], v. 3, n. 01, p. 21-27, june 2020. ISSN 0000-0000. Available at: <https://ojs.unitas-pdg.ac.id/index.php/azimut/article/view/633>. Date accessed: 30 nov. 2021.
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References

Baillarin, S.J., Meygret, A., Dechoz, C., Petrucci, B., Lacherade, S., Tremas, T., Isola, C., Martimort, P., Spoto,F., 2012. Sentinel-2 Level 1 Products and Image Processing Performances. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1, 2012 XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia

Bruce, C.M. and Hilbert, D.W. 2006. Pre-Processing Methodology for Application to Landsat TM/ETM+ Imagery of the Wet Tropics. Research Report. Rainforest Cooperative Research Center for Tropical Rainforest Ecology and Management, Australian Cooperative Research Centres Programs (CRC). www.rainforest-crc.jcu.edu.au

Danoedoro, P., 2012, Pengantar Penginderaan Jauh Digital, Penerbit Andi, Yogyakarta.

Huang, S.L. Wang, S.H. & Budd, W.W. 2009. Sprawl in Taipei’s Peri-Urban Zone: Responses to Spatial Planning and Implications for Adapting Gobal Environmental Change. Landscape and Urban Planning, 90 (1-2), pp. 20-32

Jensen, J.R., 2005, Introductory Digital Image Processing: A Remote Sensing Perspective, Third Edition, Pearson Education, Inc., United States of America

Reis, S. 2008. Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey. Sensors, 8(10), pp. 6188-62202
Song, C. Woodcock, C.E. Seto, K.C. Lenney, M.P. and Scott, A.M. 2001. Classification and Change Detection Using Landsat TM Data; When and How to Correct Atmospheric Effect?. Remote Sensing of Environment 75, pp. 230-244

Richards, J.A & Jia, X. 2006. Remote Sensing Digital Image Analysis; An Introduction. Berlin, Germany: Springer

Short, N. M. (1982). The landsat tutorial workbook — Basics of satellite remote sensing. Greenbelt, Md., Goddard Space Flight Center, NASA Reference Publication 1078.

Sutanto, 1986. Penginderaan Jauh Dasar (Jilid I). Fakultas Geografi, Universitas Gadjah Mada

Sutanto, 1994. Penginderaan Jauh Dasar (Jilid II). Fakultas Geografi, Universitas Gadjah Mada