Classification of agricultural crops from middle-resolution satellite images using Gaussian processes based method
Автор: Safonova Anastasiia N., Dmitriev Yegor V.
Журнал: Журнал Сибирского федерального университета. Серия: Техника и технологии @technologies-sfu
Статья в выпуске: 8 т.11, 2018 года.
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Agricultural applications of the Gaussian process (GP) based techniques is considered. A method of classifying crops from multi-temporal Landsat 8 satellite imagery is proposed. The method uses the model of spectral features based on GP regression with constant expectation and square exponential covariance functions. Main steps of the classification procedure and examples of recognition of culture species are represented. The ground based data are used for quantitative validation of the proposed classification method. The highest overall classification accuracy in three classes of crops is 77.78%.
Gaussian processes, classification, regression, agricultural crops, landsat images, remote sensing, ndvi, снимки landsat
Короткий адрес: https://sciup.org/146279558
IDR: 146279558 | DOI: 10.17516/1999-494X-0113
Список литературы Classification of agricultural crops from middle-resolution satellite images using Gaussian processes based method
- Schowengerdt R.A. Remote Sensing, Third Edition: Models and Methods for Image Processing. Academic Press, USA, 2006.
- Lu D., Weng Q. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 2007, 28(5), 823-870.
- Vibhute A.D., Gawali B.W. Analysis and Modeling of Agricultural Land use using Remote Sensing and Geographic Information System: a Review. International Journal of Engineering Research and Applications, 2013, 3(3), 081-091.
- Ioannis M., Meliadis M. Multi-temporal Landsat image classification and change analysis of land cover/use in the Prefecture of Thessaloiniki, Greece. Proceedings of the International Academy of Ecology and Environmental Sciences, 2011, 1(1), 15-25.
- Candade B.N. Multispectral landuse classification using neural networks and support vector machines: one or the other, or both. International Journal of Remote Sensing, 2008, 29(4), 1185-1206.
- Vapnik V. Statistical Learning Theory. Wiley, New York, 1998.
- Devadas R., Denham R.J., Pringle M. Support vector machine classification of object-based date for crop mapping, using multi-temporal Landsat imagery. XXII Congress of ISPRS, 25 August -01 September 2012, Melbourne, Australia.
- Topaloglu R.H., Sertel E., Musaoglu N. Assessment of classification accuracies of sentinel-2 and landsat-8 data for land cover/use mapping. XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic.
- Waske B., Benediktsson J.A. Fusion of Support Vector Machines for Classification of Multi-sensor Data. IEEE Transactions on geoscience and remote sensing, 2007, 45(12), 3858 -3866.
- US Geological Survey. . Access: https://www.usgs.gov/.
- Agricultural Monitoring System. . Access: http://activemap.ikit.sfukras.ru/.
- Bishop C. Pattern Recognition and Machine Learning. Springer, 2006.
- Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machine Learning. The MIT Press, London, 2006.
- Perdikaris P., Raissi M. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling. Proc. R. Soc. A, UK, 2017.
- Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Springer, 2001.
- Kavzoglu T., Colkesen I. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(5), 352-359.
- Omo-Irabor O.O. A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region. Journal of Geographic Information System, 2016, 8, 163-170.
- Singha M., Wu B., Zhang M. An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India. Remote Sens., 2016, 8, 479.
- Abbey R., He T., Wang T. Methods of Multinomial Classification Using Support Vector Machines. Paper SAS434-2017.
