@article{fdi:010068871,
  title = {{PCA} and {SVM} as geo-computational methods for geological mapping in the southern of {T}unisia, using {ASTER} remote sensing data set},
  author = {{G}asmi, {A}. and {G}omez, {C}{\'e}cile and {Z}ouari, {H}. and {M}asse, {A}. and {D}ucrot, {D}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he purpose of this study was to examine the efficiency of {A}dvanced {S}pace {B}orne {T}hermal {E}mission and {R}eflection {R}adiometer ({ASTER}) data in the discrimination of geological formations and the generation of geological map in the northern margin of the {T}unisian desert. {T}he nine {ASTER} bands covering the visible ({VIS}), near-infrared ({NIR}) and short-wave infrared ({SWIR}) spectral regions (wavelength range of 400-2500 nm) have been treated and analyzed. {A}s a first step of data processing, crosstalk correction, resampling, orthorectification, atmospheric correction, and radiometric normalization have been applied to the {ASTER} radiance data. {T}hen, to decrease the redundancy information in highly correlated bands, the principal component analysis ({PCA}) has been applied on the nine {ASTER} bands. {T}he results of {PCA} allow the validation and the rectification of the lithological boundaries already published on the geologic map, and gives a new information for identifying new lithological units corresponding to superficial formations previously undiscovered. {T}he application of a supervised classification on the principal components image using a support vector machine ({SVM}) algorithm shows good correlation with the reference geologic map. {T}he overall classification accuracy is 73 % and the kappa coefficient equals to 0.71. {T}he processing of {ASTER} remote sensing data set by {PCA} and {SVM} can be employed as an effective tool for geological mapping in arid regions.},
  keywords = {{PCA} ; {SVM} ; {ASTER} ; {G}eological mapping ; {T}unisia ; {TUNISIE}},
  booktitle = {},
  journal = {{A}rabian {J}ournal of {G}eosciences},
  volume = {9},
  numero = {20},
  pages = {art. 753 [12 p.]},
  ISSN = {1866-7511},
  year = {2016},
  DOI = {10.1007/s12517-016-2791-1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010068871},
}