@Article{LuLiMorDutBat:2011:CoMuIn,
               author = "Lu, Dengsheng and Li, Guiying and Moran, Emilio and Dutra, Luciano 
                         Vieira and Batistella, Mateus",
          affiliation = "Indiana Univ, Anthropol Ctr Training \& Res Global Environm Chan, 
                         Bloomington, IN 47405 USA and Indiana Univ, Anthropol Ctr Training 
                         \& Res Global Environm Chan, Bloomington, IN 47405 USA and 
                         Indiana Univ, Anthropol Ctr Training \& Res Global Environm Chan, 
                         Bloomington, IN 47405 USA and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and Brazilian Agr Res Corp, EMBRAPA Satellite 
                         Monitoring, BR-13070115 Sao Paulo, Brazil",
                title = "A Comparison of Multisensor Integration Methods for Land Cover 
                         Classification in the Brazilian Amazon",
              journal = "GIScience and Remote Sensing",
                 year = "2011",
               volume = "48",
               number = "3",
                pages = "345--370",
                month = "July-Sept.",
                 note = "Setores de Atividade: Agricultura, Pecu{\'a}ria, 
                         Produ{\c{c}}{\~a}o Florestal, Pesca e Aq{\"u}icultura.",
             keywords = "Radar de Abertura Sint{\'e}tica, radar, Digital Image 
                         Processing.",
             abstract = "Many data fusion methods are available, but it is poorly 
                         understood which fusion method is suitable for integrating Landsat 
                         Thematic Mapper (TM) and radar data for land cover classification. 
                         This research explores the integration of Landsat TM and radar 
                         images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land 
                         cover classification in a moist tropical region of the Brazilian 
                         Amazon. Different data fusion methods-principal component analysis 
                         (PCA), wavelet-merging technique (Wavelet), high-pass filter 
                         resolution-merging (HPF), and normalized multiplication (NMM)-were 
                         explored. Land cover classification was conducted with maximum 
                         likelihood classification based on different scenarios. This 
                         research indicates that individual radar data yield much poorer 
                         land cover classifications than TM data, and PALSAR L-band data 
                         perform relatively better than RADARSAT-2 C-band data. Compared to 
                         the TM data, the Wavelet multisensor fusion improved overall 
                         classification by 3.3%-5.7%, HPF performed similarly, but PCA and 
                         NMM reduced overall classification accuracy by 5.1%-6.1% and 7.6% 
                         -12.7%, respectively. Different polarization options, such as HH 
                         and HV, work similarly when used in data fusion. This research 
                         underscores the importance of selecting a suitable data fusion 
                         method that can preserve spectral fidelity while improving spatial 
                         resolution.",
                  doi = "10.2747/1548-1603.48.3.345",
                  url = "http://dx.doi.org/10.2747/1548-1603.48.3.345",
                 issn = "1548-1603",
                label = "lattes: 9840759640842299 4 LuLiMorDutBat:2011:CoMuIn",
             language = "en",
        urlaccessdate = "28 abr. 2024"
}