Advanced Classification of Remote Sensing High Resolution Imagery. An Application for the Management of Natural Resources

  • Edurne Ibarrola-Ulzurrun
  • Javier Marcello
  • Consuelo Gonzalo-Martin
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 718)


In the last decades, there has been a decline in ecosystems natural resources. The objective of the study is to develop advanced image processing techniques applied to high resolution remote sensing imagery for the ecosystem conservation. The study area is focused in three ecosystems from The Canary Islands, Teide National Park, Maspalomas Natural Reserve and Corralejo and Islote de Lobos Natural Park. Different pre-processing steps have been applied in order to acquire high quality imagery. After an extensive analysis and evaluation of pansharpening techniques, Weighted Wavelet ‘à trous’ through Fractal Dimension Maps, in Teide and Maspalomas scenes, and Fast Intensity Hue Saturation, in Corralejo scene, are used, then, a RPC (Rational Polymodal Coefficients) model performs the orthorectification and finally, the atmospheric correction is carried out by the 6S algorithm. The final step is to generate marine and terrestrial thematic products using advanced classification techniques for the management of natural resources. Accurate thematic maps have already been obtained in Teide National Park. A comparative study of both pixel-based and object-based (OBIA) approaches was carried out, obtaining the most accurate thematic maps in both of them using Support Vector Machine classifier.


Remote sensing Natural resources Image pre-processing High resolution image Pansharpening Orthorectification Atmospheric correction Classification OBIA 



This research has been supported by the ARTEMISAT (CGL2013-46674-R) project, funded by the Spanish Ministerio de Economía y Competitividad). Moreover, the authors want to acknowledge to the Teide National Park conservation managers (Jose Luis Martín Esquivel y Manuel Marrero Gómez) for defining the classes of interest and examining the results obtained. This work was completed while E. I-U was a Ph.D. student in the IOCAG Doctoral Program in Oceanography and Global Change and funded by the Spanish Ministerio de Economía y Competitividad with a FPI grant (BES-2014-069426).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Edurne Ibarrola-Ulzurrun
    • 1
    • 2
  • Javier Marcello
    • 1
  • Consuelo Gonzalo-Martin
    • 2
  1. 1.Instituto de Oceanografía y Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria, ULPGCLas Palmas de Gran CanariaSpain
  2. 2.Facultad de Informática, Departamento de Arquitectura y Tecnología de Sistemas InformáticosUniversidad Politécnica de MadridMadridSpain

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