Clasificación de usos del suelo a partir de imágenes Sentinel-2

  1. Borràs, J.
  2. Delegido, J.
  3. Pezzola, A.
  4. Pereira, M.
  5. Morassi, G.
  6. Camps-Valls, G.
Journal:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Year of publication: 2017

Issue: 48

Pages: 55-66

Type: Article

DOI: 10.4995/RAET.2017.7133 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de teledetección: Revista de la Asociación Española de Teledetección

Sustainable development goals

Abstract

Sentinel-2 (S2), a new ESA satellite for Earth observation, accounts with 13 bands which provide high-quality radiometric images with an excellent spatial resolution (10 and 20 m) ideal for classification purposes. In this paper, two objectives have been addressed: to determine the best classification method for S2, and to quantify its improvement with respect to the SPOT operational mission. To do so, four classifiers (LDA, RF, Decision Trees, K-NN) have been selected and applied to two different agricultural areas located in Valencia (Spain) and Buenos Aires (Argentina). All classifiers were tested using, on the one hand, all the S2 bands and, on the other hand, only selecting those bands from S2 closer to the four bands from SPOT. In all the cases, between 10%-50% of samples were used to train the classifier while remaining the rest for validation. As a result, a land use map was generated from the best classifier, according to the Kappa index, providing scientifically relevant information such as the area of each land use class.

Bibliographic References

  • Abraira, V. 2001. El índice kappa. Semergen, 27(5), 247-249. https://doi.org/10.1016/S1138- 3593(01)73955-X
  • Breiman, L. 1984. Classification and regression trees. Chapman & Hall/CRC. Breiman, L. 2001. Random forests. Machine Learning, 45(1), 5-32. https://doi. org/10.1023/A:1010933404324
  • Camps-Valls, G., Gómez Chova, L., Muñoz Marí, J., Rojo Álvarez, J.L., Martínez-Ramón, M. 2008. Kernel based framework for muli-temporal and multi-source remote sensing data classification and change detection. IEEE Trans. Geosc. Rem. Sens., 46(6), 1822-1835. https://doi.org/10.1109/ TGRS.2008.916201
  • Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., Malo, J. 2011. Remote Sensing Image Processing. Synthesis Lectures on Image, Video, and Multimedia Processing, 5(1), 1-192. https://doi.org/10.2200/ S00392ED1V01Y201107IVM012
  • Cohen J. A. 1960. Coefficient of agreement for nominal scales. Educ. Psychol. Meas., 20(1), 37-46. https:// doi.org/10.1177/001316446002000104
  • Comber, A., Fisher, P., Wadsworth, R. 2005. You know what land cover is but does anyone else? ... an investigation into semantic and ontological confusion. International Journal of Remote Sensing, 26(1), 223- 228. https://doi.org/10.1080/0143116042000274032
  • Del Bosque, I., Arozarena, A., Villa, G., Valcárcel, N., Porcuna, A. 2005. Creación de un sistema de información geográfico de ocupación del suelo en España."Proyecto SIOSE". Actas del XI Congreso Nacional de Teledetección, 21-23 Septiembre, Puerto de la Cruz, España, 255-262.
  • Delegido, J., Verrelst, J., Alonso, L., Moreno, J. 2011. Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content. Sensors, 11, 7063-7081. https://doi. org/10.3390/s110707063
  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernández, V., Gascón, 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. https://doi.org/10.1016/j. rse.2011.11.026
  • ESA. 2016. www.esa.int/. Consultado el 10 de julio de 2016.
  • Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R. 2006. Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300. https:// doi.org/10.1016/j.patrec.2005.08.011
  • Hastie, T., Tibshirani, R., Friedman, J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer: New York, NY, USA. https://doi.org/10.1007/978-0-387- 84858-7
  • Immitzer, M., Atzberger, C., Koukal, T. 2012. Tree species classification with Random Forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sensing, 4, 2661-2693. https:// doi.org/10.3390/rs4092661
  • James, G., Witten, D., Hastie, T., Tibshiram, R. 2015. An Introduction to Statistical Learning with Applications in R. In Springer Texts in Statistics. Volume 103. https://doi.org/10.1007/978-1-4614- 7138-7
  • Landis, J.R., Koch, G.G. 1977. The measurement of observer agreement for categorical data. Biometrics, 33, 159-174. https://doi.org/10.2307/2529310
  • López González, F.J., Crecente Maseda, R., Álvarez López, C.J. 2002. Los usos del suelo analizados mediante S.I.G. XIV Congreso de Ingeniería Gráfica. 5-7 Junio, Santander, España.
  • Manual de usuario de Sentinel 2-ESA. Julio 2015.
  • Martimort, P., Berger, M., Carnicero, B., Del Bello, U., Fernández, V., Gascón, F., et al. 2007. Sentinel-2: Optical high-resolution mission for GMES operational services. Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International, 131, 18-23. https://doi.org/10.1109/ igarss.2007.4423394
  • Mena, A.J. 2014. Procesamiento de imágenes satelitales multiespectrales. Proyecto final de carrera, Facultad de Informática, Universidad del País Vasco.
  • Pezzola, A. 2014. Transformaciones territoriales producto del riego en el Valle Bonaerense del Río Colorado. Departamento de geografía y turismo, UNS, provincia de Buenos Aires.
  • Quinlan, J.R. 1993. Programs for Machine Learning. 1st ed. San Mateo, CA, Morgan.
  • Rees, G. 2005. The Remote Sensing Data Book. Cambridge University Press, 262 pp.
  • Rodríguez-Galiano, V., Chica-Rivas, M. 2012. Clasificación de imágenes de satélite mediante software libre: Nuevas tendencias en algoritmos de Inteligencia artificial. Departamento de Geodinámica, Universidad de Granada.
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