Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Boudewijn van Leeuwen 1 , Zalán Tobak 1 ,  and Ferenc Kovács 1
  • 1 Department of Physical Geography and Geoinformatics, University of Szeged, Szeged, Hungary

Abstract

Classification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

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  • Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jozefowicz, R., Jia, Y., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Schuster, M., Monga, R., Moore, S., Murray, D., Olah, C., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan,V., Viégas F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X. 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.

  • Baamonde, S., Cabana, M., Sillero N., Penedo, M.G., Naveira, H., Novo, J. 2019. Fully automatic multi-temporal land cover classification using Sentinel-2 image data. Procedia Computer Science 159, 650–657. DOI: 10.1016/j.procs.2019.09.220

  • Balázs, B., Bíró, T., Dyke, G., Singh, S.K., Szabó, Sz. 2018. Extracting water-related features using reflectance data and principal component analysis of Landsat images. Hydrological Sciences Journal 63(2), 269–284. DOI: 10.1080/02626667.2018.1425802

  • Breiman, L. 2001. Random Forests. Machine Learning 45(5–32). DOI:10.1023/A:1010933404324

  • Büttner, G., 2012. Guidelines for verification and enhancement of high resolution layers produced under GMES initial operations (GIO) Land monitoring 2011–2013. EEA Report

  • Chatziantoniou, A., Petropoulos, G.P., Psomiadis E. 2017. Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning. Remote Sensing 9, 1259. DOI:10.3390/rs9121259

  • Chollet, F. 2015. Keras, https://keras.io [04-20-2020]

  • CLC, 2018. Corine Land Cover (CLC) 2018, Version 20. European Environment Agency. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 [04-20-2020]

  • Congalton, R.G., Green, K. 2008. Assessing the accuracy of remotely sensed data: principles and practices. CRC, Boca Raton London New York, 183 p

  • Csendes, B. Mucsi, L. 2016. Inland excess water mapping using hyperspectral imagery. Geographica Pannonica 20 (4), 191–196. DOI: 10.18421/GP20.04-01

  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P. Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., Bargellini, P. 2012. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment 120, 25–36, DOI: 10.1016/j.rse.2011.11.026

  • Feyisa, G.L., Meilby, H., Fensholt, R., Proud, S.R. 2014. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140, 23–35

  • Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, D.J., Hughes, M.J., Laue, B. 2017. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment 194, 379–390, DOI: 10.1016/j.rse.2017.03.026

  • Gudmann, A., Mucsi, L., Henits, L. 2019. A CORINE felszínborítási térkép automatikus előállításának lehetősége döntésifa-osztályozó segítségével. (Automatic land cover mapping using decision tree classifier). Geodézia és Kartográfia 71(2), 9–13. (in Hungarian)

  • Huang, C., Davis, L.S., Townshend, J.R.G. 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23(4), 725–749, DOI: 10.1080/01431160110040323

  • Jin, Y., Liu, X., Chen, Y., Liang, X. 2018. Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong. International Journal of Remote Sensing 39 (23), 8703–8723, DOI: 10.1080/01431161.2018.1490976

  • Lacaux, J.P., Tourre, Y.M., Vignolles, C., Ndione, J.A., Lafaye, M. 2007. Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment 106, 66–74. DOI: 10.1016/j.rse.2006.07.012

  • Mezősi G. 2017. Physical Geography of Hungary. Heidelberg, London, New York, Springer, 334 p

  • Ming, D., Zhou, T., Wang, M., Tan, T. 2016. Land cover classification using random forest with genetic algorithm-based parameter optimization. J. Appl. Remote Sens. 10 (3), 035021. DOI: 10.1117/1.jrs.10.035021

  • Mucsi, L., Henits, L. 2010. Creating excess water inundation maps by sub-pixel classification of medium resolution satellite images. Journal of Environmental Geography 3 (1–4), 31–40.

  • Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S. 2019. PyTorch: An imperative style high-performance deep learning library. Proc. Adv. Neural Inf. Process. Syst. 32, 8024–8035.

  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.,Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830

  • Rai, A.K., Mandal, N., Singh, A., Singh, K.K. 2020. Landsat 8 OLI Satellite Image Classification using Convolutional Neural Network. Procedia Computer Science 167, 987–993. DOI:10.1016/j.procs.2020.03.398

  • Rakonczai, J., Mucsi, L., Szatmári, J., Kovács, F., Csató, Sz. 2001. A belvizes területek elhatárolásának módszertani lehetőségei (Methods for delineation of inland excess water areas). A földrajz eredményei az új évezred küszöbén. Az I. Magyar Földrajzi Konferencia CD 14 p. (in Hungarian)

  • Shahtahmassebi, A., Yang, N., Wang, K., Moore, N., Shen, Z. 2013. Review of shadow detection and de-shadowing methods in remote sensing. Chinese Geographical Science 23, 403–420. DOI: 10.1007/s11769-013-0613-x

  • Shi D., Yang, X. 2015. Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery. In: Li, J., Yang X. (eds.) Monitoring and Modeling of Global Changes: A Geomatics Perspective. Springer Remote Sensing/Photogrammetry, Dordrecht, DOI: 10.1007/978-94-017-9813-6_13

  • Szántó, G., Mucsi, L., van Leeuwen, B. 2008. Application of self-organizing neural networks for the delineation of excess water areas. Journal of Env. Geogr. 1 (3-4), 15–20.

  • Szantoi Z, Escobedo FJ, Abd-Elrahman A, Pearlstine L, Dewitt B, Smith S. 2015, Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features. Environ Monit Assess 187 (5), 262. DOI: 10.1007/s10661-015-4426-5

  • Szatmári, J., van Leeuwen, B. 2013. Inland Excess Water – Belvíz – Suvišne Unutrašnje Vode, Szeged, Újvidék, Szegedi Tudományegyetem, Újvidéki Egyetem, 154 p

  • Tanács E., Belényesi M., Lehoczki R., Pataki R., Petrik O., Standovár T., Pásztor L., Laborczi A., Szatmári G., Molnár Zs., Bede-Fazekas Á., Kisné Fodor L., Varga I., Zsembery Z., Maucha G. 2019. Országos, nagyfelbontású ökoszisztéma- alaptérkép: módszertan, validáció és felhasználási lehetőségek. (National high resolution ecosystem base map: Methodology, validation and possibilities for applications). Természetvédelmi közlemények 25, 34–58. DOI: 10.17779/tvkjnatconserv.2019.25.34. (in Hungarian)

  • Thanh-Noi, P., Kappas, M. 2018. Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors 18 (2), 18. DOI: 10.3390/s18010018

  • van Leeuwen, B., Mezősi, G., Tobak, Z., Szatmári, J., Barta, K. 2012. Identification of inland excess water floodings using an artificial neural network. Carpathian Journal of Earth and Environmental Sciences 7 (4), 173–180.

  • van Leeuwen, B., Tobak, Z. 2014. Operational Identification of Inland Excess Water Floods Using Satellite Imagery, In: Vogler, R., Car, A., Strobl, J.Griesebner, G. (Eds.), GI_Forum 2014. Geospatial Innovation for Society. Herbert Wichmann Verlag, VDE Verlag GMBH, Berlin/Offenbach, 12–15. DOI: 10.1553/giscience2014s12

  • van Leeuwen, B., Tobak, Z., Kovács, F. 2020. Sentinel 1 and 2 based near real time inland excess water mapping for optimized water management. Sustainability 12 (7), 2854. DOI: 10.3390/su12072854

  • Zhu, X.X., Tuia, D., Mou, L., Xia, G-S.,Zhang, L., Xu, F., Fraundorfer, F. 2017. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources. IEEE Geoscience and Remote Sensing Magazine 5(4) 8–36. DOI: 10.1109/mgrs.2017.2762307

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