Journal of Applied Geospatial Information
Vol 5 No 2 (2021): Journal of Applied Geospatial Information (JAGI)

WorldView-2 Satellite Image Classification using U-Net Deep Learning Model

Ilyas Ilyas (Dept. of Geomatics Engineering, Institut Teknologi Sumatera (ITERA), Jalan Terusan Ryacudu, Way Hui, Kecamatan Jati Agung, Lampung Selatan 35365, Indonesia)
Lalu Muhamad Jaelani (Dept. of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya)
Muhammad Aldila Syariz (Dept. of Geomatics Engineering, Institut Teknologi Sepuluh Nopember (ITS), Kampus ITS, Surabaya, Indonesia)
Husnul Hidayat (Dept. of Geomatics Engineering, Institut Teknologi Sepuluh Nopember (ITS), Kampus ITS, Surabaya, Indonesia)

Article Info

Publish Date
08 Sep 2021


Land cover maps are important documents for local governments to perform urban planning and management. A field survey using measuring instruments can produce an accurate land cover map. However, this method is time-consuming, expensive, and labor-intensive. A number of researchers have proposed using remote sensing, which generates land cover maps using an optical satellite image with various statistical classification procedures. Recently, artificial intelligence (AI) technology, such as deep learning, has been used in multiple fields, including satellite image classification, with satisfactory results. In this study, a WorldView-2 image of Terangun in Aceh Province, which was acquired on Aug 2, 2016, was classified using a commonly used deep-learning-based classification, namely, U-net. There were eight classes used in the experiment: building, road, open land (such as green open space, bare land, grass, or low vegetation), river, farm, field, aquaculture pond, and garden. For comparison, three classification methods: maximum-likelihood, random forest, and support vector machine, were performed compared to U-Net. A land cover map provided by the government was used as a reference to evaluate the accuracy of land cover maps generated using two classification methods. The results with 100 randomly selected pixels revealed that U-Net was able to obtain a 72% and 0.585 for overall and kappa accuracy, respectively; whereas, overall accuracy and kappa accuracy for the maximum likelihood, random forest and support vector machine methods were 49% and 0.148; 59% and 0.392; and 67% and 0. 511; respectively. Therefore, U-Net outperformed those three of classification methods in classifying the image.

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Journal Info





Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Engineering Environmental Science Physics


Journal of Applied Geospatial Information (JAGI) is a national and international peer review journal published by Politeknik Negeri Batam. The JAGI is issued 2 times a year in electronic form, publishes Original Research Articles (full papers and short communications) and Review (full and mini ...