@InProceedings{OliveiraOliCarMarFre:2009:CoMOND,
               author = "Oliveira, Thomaz Chaves de Andrade and Oliveira, Luciano Teixeira 
                         de and Carvalho, Luis Marcelo Tavares de and Martinhago, Adriana 
                         Zanella and Freitas, S{\'a}vio Gouv{\^e}a de",
          affiliation = "{Universidade Federal de Lavras} and {Universidade Federal de 
                         Lavras} and {Univeridade Federal de Lavras} and {Universidade 
                         Federal de Lavras} and {Universidade Federal de Lavras}",
                title = "Comparison of MODIS NDVI Time Series filtering by Wavelets and 
                         Fourier analysis to Generate Vegetation Signatures",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "1465--1472",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "sensing, image processing, time series, wavelets analysis, NDVI, 
                         MODIS, Fourier.",
             abstract = "Temporal vegetation signatures (i. e., vegetation indices as 
                         functions of time) generated using the MODIS instrument poses many 
                         challenges, primarily due to signal to noise-related issues Bruce 
                         et al. (2006). This study investigates which methods best generate 
                         smoothed curves of vegetation signatures on MODIS NDVI time 
                         series. The filtering techniques compared were the HANTS 
                         algorithm, Verhoef (1996), which is based on Fourier analyses and 
                         Wavelet temporal algorithm which uses the wavelet analysis to 
                         generate the smoothed curves. The smoothed data were used as input 
                         data vectors for vegetation classification by means of Artificial 
                         neural networks. Statistics of the classifications reveal that the 
                         Wavelet filtering algorithm outperforms the original time series 
                         and the HANTS fft derived algorithms in all cases in all the 
                         classification algorithms.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.18.12.12",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.18.12.12",
           targetfile = "1465-1472.pdf",
                 type = "An{\'a}lise e Aplica{\c{c}}{\~a}o de Imagens Multitemporais",
        urlaccessdate = "11 maio 2024"
}