@Article{LiLuMorDutBat:2012:CoAnAL,
               author = "Li, Guiying and Lu, Dengsheng and Moran, Emilio and Dutra, Luciano 
                         Vieira and Batistella, Mateus",
          affiliation = "Indiana University, Anthropological Center for Training and 
                         Research on Global Environmental Change, Bloomington, Indiana 
                         47405 and Indiana University, Anthropological Center for Training 
                         and Research on Global Environmental Change, Bloomington, Indiana 
                         47405 and Indiana University, Anthropological Center for Training 
                         and Research on Global Environmental Change, Bloomington, Indiana 
                         47405 and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band 
                         data for land-cover classification in a tropical moist region",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2012",
               volume = "70",
                pages = "26--38",
                month = "June",
             keywords = "ALOS PALSAR, RADARSAT, Texture, Land-cover classification, 
                         Amazon.",
             abstract = "This paper explores the use of ALOS (Advanced Land Observing 
                         Satellite) PALSARL-band (Phased Array type L-band Synthetic 
                         Aperture Radar) and RADARSAT-2 C-band data for land-cover 
                         classification in a tropical moist region. Transformed divergence 
                         was used to identify potential textural images which were 
                         calculated with the gray-level co-occurrence matrix method. The 
                         standard deviation of selected textural images and correlation 
                         coefficients between them were then used to determine the best 
                         combination of texture images for land-cover classification. 
                         Classification results based on different scenarios with maximum 
                         likelihood classifier were compared. Based on the identified best 
                         scenarios, different classification algorithms maximum likelihood 
                         classifier, classification tree analysis, Fuzzy ARTMAP (a 
                         neural-network method), k-nearest neighbor, object-based 
                         classification, and support vector machine were compared for 
                         examining which algorithm was suitable for land-cover 
                         classification in the tropical moist region. This research 
                         indicates that the combination of radiometric images and their 
                         textures provided considerably better classification accuracies 
                         than individual datasets. The L-band data provided much better 
                         landcover classification than C-band data but neither L-band nor 
                         C-band was suitable for fine land-cover classification system, no 
                         matter which classification algorithm was used. L-band data 
                         provided reasonably good classification accuracies for coarse 
                         land-cover classification system such as forest, succession, 
                         agropasture, water, wetland, and urban with an overall 
                         classification accuracy of 72.2%, but C-band data provided only 
                         54.7%. Compared to the maximum likelihood classifier, both 
                         classification tree analysis and Fuzzy ARTMAP provided better 
                         performances, object-based classification and support vector 
                         machine had similar performances, and k-nearest neighbor performed 
                         poorly. More research should address the use of multitemporal 
                         radar data and the integration of radar and optical sensor data 
                         for improving land-cover classification.",
                  doi = "10.1016/j.isprsjprs.2012.03.010",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2012.03.010",
                 issn = "0924-2716 and 1872-8235",
                label = "lattes: 9840759640842299 4 LiLuMorDutBat:2012:CoAnAL",
             language = "en",
           targetfile = "Guiying_et_al_2012.pdf",
        urlaccessdate = "20 maio 2024"
}