@article{fdi:010071932,
  title = {{C}an the global modeling technique be used for crop classification ?},
  author = {{M}angiarotti, {S}ylvain and {S}harma, {A}. {K}. and {C}orgne, {S}. and {H}ubert-{M}oy, {L}. and {R}uiz, {L}aurent and {S}ekhar, {M}. and {K}err, {Y}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}rop detection from remote sensed images is of major interest for land use and land cover mapping. {C}lassification techniques often require multi-temporal images. {H}owever, most of these techniques assume that the cultural cycle occurs at the same dates across plots or for a given crop and do not take into account the sensitivity to initial conditions of the dynamical behaviors. {S}uch hypotheses are not well adapted when a wide diversity of practices is observed for the same crops from one crop field to another, which is often the case in tropical context. {T}o cope with these difficulties, a new classification technique based on the global modeling technique is introduced in this paper. {I}t is first applied to a case study based on chaotic oscillators. {I}t is then tested on crop classification observed from satellite data. {T}he {B}erambadi watershed ({S}outh {I}ndia) is taken as a case study to test this new classification approach. {C}rop classification is a difficult problem in {S}outhern {I}ndia where optical satellite images are scarce during the monsoon season due to cloud cover, and where crop land is divided in parcels (i.e. crop fields) of very small sizes with diversified crops. {T}he {L}andsat-8 images were used to monitor an ensemble of 104 parcels of ten different crops (irrigated and non-irrigated). {U}sing global modeling, a bank of crop models was first obtained for the ten crops considered in the study. {A} metric is introduced to compare the observed signal to the obtained crop-models used as reference for each crop dynamic. {B}ased on this metric, the possibility to use global models as references for distinguishing crops is investigated. {T}he results provide a good proof-of-concept and show promising potential for crop classification.},
  keywords = {{C}rop identification ; {G}lobal modeling ; {T}ime series ; {C}lassification ; {L}and use ; {C}rop phenology ; {INDE}},
  booktitle = {},
  journal = {{C}haos {S}olitons and {F}ractals},
  volume = {106},
  numero = {},
  pages = {363--378},
  ISSN = {0960-0779},
  year = {2018},
  DOI = {10.1016/j.chaos.2017.12.003},
  URL = {https://www.documentation.ird.fr/hor/fdi:010071932},
}