1. bookVolume 13 (2020): Issue 1-2 (April 2020)
Journal Details
License
Format
Journal
First Published
25 Apr 2013
Publication timeframe
2 times per year
Languages
English
access type Open Access

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

Published Online: 29 May 2020
Page range: 43 - 52
Received: 01 Apr 2020
Accepted: 01 May 2020
Journal Details
License
Format
Journal
First Published
25 Apr 2013
Publication timeframe
2 times per year
Languages
English

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.

Keywords

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.Search in Google Scholar

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.2019.09.220Search in Google Scholar

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: 10.1080/02626667.2018.1425802Search in Google Scholar

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

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

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/rs9121259Search in Google Scholar

Chollet, F. 2015. Keras, https://keras.io [04-20-2020]Search in Google Scholar

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]Search in Google Scholar

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

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

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.2011.11.026Search in Google Scholar

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, 23–35Search in Google Scholar

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.2017.03.026Search in Google Scholar

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)Search in Google Scholar

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: 10.1080/01431160110040323Search in Google Scholar

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: 10.1080/01431161.2018.1490976Search in Google Scholar

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.2006.07.012Search in Google Scholar

Mezősi G. 2017. Physical Geography of Hungary. Heidelberg, London, New York, Springer, 334 pSearch in Google Scholar

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.10.035021Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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, 2825–2830Search in Google Scholar

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.2020.03.398Search in Google Scholar

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)Search in Google Scholar

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-xSearch in Google Scholar

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_13Search in Google Scholar

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.Search in Google Scholar

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/s10661-015-4426-5Search in Google Scholar

Szatmári, J., van Leeuwen, B. 2013. Inland Excess Water – Belvíz – Suvišne Unutrašnje Vode, Szeged, Újvidék, Szegedi Tudományegyetem, Újvidéki Egyetem, 154 pSearch in Google Scholar

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)Search in Google Scholar

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/s18010018Search in Google Scholar

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.Search in Google Scholar

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/giscience2014s12Search in Google Scholar

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/su12072854Search in Google Scholar

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.2017.2762307Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo