@techreport{TR-IC-09-47,
   number = {IC-09-47},
   author  =  {Jefersson  Alex {dos Santos} and Otávio Augusto Bizetto
                   {Penatti} and Ricardo da Silva {Torres}},
   title  = {Evaluating the Potential of Texture and Color Descriptors
                   for    Remote    Sensing    Image   Retrieval   and  
                   Classification},
   month = {December},
   year = {2009},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 13 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Classifying  Remote  Sensing Images (RSI) is a hard task. There
       are  automatic  approaches  whose  results  normally need to be
       revised.   The  identification  and  polygon  extraction  tasks 
       usually rely on applying classification strategies that exploit
       visual   aspects  related  to  spectral  and  texture  patterns 
       identified in RSI regions. There are a lot of image descriptors
       proposed  in  the  literature for content-based image retrieval
       purposes  that can be useful for RSI classification. This paper
       presents a comparative study to evaluate the potential of using
       successful  color  and  texture  image  descriptors  for remote
       sensing  retrieval  and  classification. Seven descriptors that
       encode  texture  information  and twelve color descriptors that
       can  be  used  to encode spectral information were selected. We
       highlight  the  main characteristics and perform experiments to
       evaluate  the  effectiveness  of these descriptors. To evaluate
       descriptors   in  classification  tasks,  we  also  proposed  a 
       methodology  based  on  KNN classifier. Experiments demonstrate
       that  Joint  Auto-Correlogram  (JAC),  Color  Bitmap, Invariant
       Steerable  Pyramid  Decomposition  (SID) and Quantized Compound
       Change Histogram (QCCH) yield the best results.
  }
}