Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns

  • Ana Cláudia TeodoroEmail author
  • Francisco Gutierres
  • Pedro Gomes
  • Jorge Rocha
Part of the Coastal Research Library book series (COASTALRL, volume 24)


Remote sensing data and image classification algorithms can be very useful in the identification of beach patterns and therefore can be used as inputs in beach classification models. In this work, one aerial photograph, one IKONOS-2 image and one FORMOSAT-2 image were applied to a part of the northwest coast of Portugal. Several image processing algorithms were employed and compared: pixel-based approach, object-based approach, Principal Components Analysis (PCA), Artificial Neural Network (ANN) and Decision Trees (DT). The ANN and DT algorithms employed conduced to better results than the traditional classification methodologies (pixel-based, object-based and PCA), allowed a more accurate identification of rip currents. Regarding the data used, the high spatial resolution of aerial photograph allows for the better discrimination of different micro patterns. The FORMOSAT-2 image presents a lower spatial resolution, which did not allow for the identification of small microforms. Concluding, the conjugation of better spatial and spectral resolution of IKONOS-2 data and the data mining algorithms seems to be the better approach to accurately identify beach patterns through remotely sensed data.


Satellite images Image classification PCA Data mining OBIA 



The authors would like to thank European Space Agency (ESA) for providing the IKONOS-2 image and National Space Organization, National Applied Research Laboratories of Taiwan for provided the FORMOSAT-2 image.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ana Cláudia Teodoro
    • 1
    Email author
  • Francisco Gutierres
    • 2
  • Pedro Gomes
    • 3
  • Jorge Rocha
    • 4
  1. 1.Earth Sciences Institute (ICT) and Department of Geosciences, Environment and Land Planning, Faculty of SciencesUniversity of PortoPortoPortugal
  2. 2.Big Data Analytics UnitEurecat – Technology Centre of CataloniaBarcelonaSpain
  3. 3.Department of Environment and AgricultureNational Statistics InstituteLisbonPortugal
  4. 4.Institute of Geography and Spatial PlanningUniversidade de LisboaLisboaPortugal

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