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
Keywords: artificial intelligence, deep learning, landcover, sustainability, U-net, WorldView-2


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.



Download data is not yet available.


Badan Informasi Geospasial, 2020. Technical specifications of the base map for the preparation of the detailed spatial plan (spesifikasi teknis peta dasar untuk penyusunan rencana detail tata ruang) [WWW Document]. URL Pemeriksaan Peta RDTR/Sumber Data Citra dan Peta Dasar/V3_Spek Teknis Data Dasar dan Peta Dasar RDTR.pdf

Belward, A.S., Skøien, J.O., 2015. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS J. Photogramm. Remote Sens. 103, 115–128.

Digital Globe, 2013. Our Constellation - DigitalGlobe [WWW Document]. URL (accessed 10.26.16).

Digital Globe, 2010. Radiometric Use of WorldView-2 Imagery Technical Note.

Esri, n.d. How U-net works? | ArcGIS for Developers [WWW Document]. URL (accessed 9.7.20).

Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R., 2006. Random forests for land cover classification, in: Pattern Recognition Letters.

Gómez, C., White, J.C., Wulder, M.A., 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogramm. Remote Sens.

He, K., Gkioxari, G., Dollár, P., Girshick, R., 2020. Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell.

Hogg, R. V., 1979. Statistical Robustness: One View of Its Use in Applications Today. Am. Stat. 33, 108.

Huang, C., Davis, L.S., Townshend, J.R.G., 2002. An assessment of support vector machines for land cover classification. Int. J. Remote Sens.

Kohl, S.A.A., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J.R., Maier-Hein, K.H., Ali Eslami, S.M., Rezende, D.J., Ronneberger, O., 2018. A probabilistic U-net for segmentation of ambiguous images, in: Advances in Neural Information Processing Systems.

Lues, K.G., 2013. Qanun Kabupaten Gayo Lues Nomor 15 Tahun 2013 tentang Rencana Tata Ruang Wilayah Kabupaten Gayo Lues Tahun 2012-2032. Indonesia.

Memarian, H., Balasundram, S.K., Khosla, R., 2013. Comparison between pixel- and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery. J. Appl. Remote Sens. 7, 073512.

Mohajane, M., Essahlaoui, A., Oudija, F., El Hafyani, M., Hmaidi, A. El, El Ouali, A., Randazzo, G., Teodoro, A.C., 2018. Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5, 131.

Nguyen, H.T.T., Doan, T.M., Tomppo, E., McRoberts, R.E., 2020. Land use/land cover mapping using multitemporal sentinel-2 imagery and four classification methods-A case study from Dak Nong, Vietnam. Remote Sens. 12, 1367.

Pal, M., Mather, P.M., 2005. Support vector machines for classification in remote sensing. Int. J. Remote Sens.

Ronneberger, O., Fischer, P., Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp. 234–241.

Stoian, A., Poulain, V., Inglada, J., Poughon, V., Derksen, D., 2019. Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems. Remote Sens. 11, 1986.

Sun, J., Yang, J., Zhang, C., Yun, W., Qu, J., 2013. Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method. Math. Comput. Model. 58, 573–581.

Xing, H., Meng, Y., Hou, D., Song, J., Xu, H., 2017. Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model. Remote Sens. 2017, Vol. 9, Page 602 9, 602.

Xu, W., Deng, X., Guo, S., Chen, J., Sun, L., Zheng, X., Xiong, Y., Shen, Y., Wang, X., 2020. High-resolution u-net: Preserving image details for cultivated land extraction. Sensors (Switzerland) 20, 1–23.

Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., Zhang, S., 2018. Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors 18, 3717.