@article{fdi:010069974,
  title = {{O}ntology-based classification of remote sensing images using spectral rules},
  author = {{A}ndres, {S}. and {A}rvor, {D}. and {M}ougenot, {I}. and {L}ibourel, {T}. and {D}urieux, {L}aurent},
  editor = {},
  language = {{ENG}},
  abstract = {{E}arth {O}bservation data is of great interest for a wide spectrum of scientific domain applications. {A}n enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. {H}owever, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. {I}n this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. {W}e implemented an ontology-based prototype to automatically classify {L}andsat images based on explicit spectral rules. {T}he ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. {T}he prototype was tested on four subsets of {L}andsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.},
  keywords = {{O}ntology ; {E}xpert knowledge ; {R}emote sensing ; {I}mage classification ; {D}escription logics},
  booktitle = {},
  journal = {{C}omputers and {G}eosciences},
  volume = {102},
  numero = {},
  pages = {158--166},
  ISSN = {0098-3004},
  year = {2017},
  DOI = {10.1016/j.cageo.2017.02.018},
  URL = {https://www.documentation.ird.fr/hor/fdi:010069974},
}