@Article{NegriDutrFreiLu:2016:ExCaAL,
               author = "Negri, Rogerio Galante and Dutra, Luciano Vieira and Freitas, 
                         Corina da Costa and Lu, Dengsheng",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Michigan State University}",
                title = "Exploring the capability of ALOS PALSAR L-band fully polarimetric 
                         data for land cover classification in tropical environments",
              journal = "IEEE Journal of Selected Topics in Applied Earth Observations and 
                         Remote Sensing",
                 year = "2016",
               volume = "9",
               number = "12",
                pages = "5369--5384",
                month = "Dec.",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "Analise de Imagens, Radar de Abertura Sint{\'e}tica, Amazonia, 
                         Amazon, assessment, image classification, polarimetric synthetic 
                         aperture radar (PolSAR), scenarios, synthetic aperture radar 
                         (SAR).",
             abstract = "Among different applications using synthetic aperture radar (SAR) 
                         data, land cover classification of rain forest areas has been 
                         investigated. Previous results showed that L-band is more 
                         appropriate for such applications. However, SAR images have 
                         limited discriminability for mapping large sets of classes 
                         compared with optical imagery. The objective of this study was to 
                         carry out an analysis about the discriminative capability of an 
                         L-band fully polarimetric SAR complex image, compared to the 
                         possible subsets of polarizations in amplitude/intensity, for 
                         mapping land cover classes in Amazon regions. Two case studies 
                         using ALOS PALSAR L-band fully polarimetric images over Brazilian 
                         Amazon regions were considered. Several thematic classes, 
                         organized into scenarios, were considered for each case study. 
                         These scenarios represent distinct classification tasks with 
                         variated complexities. Performing a simultaneous analysis of 
                         different scenarios is a distinct way to assess the discriminative 
                         capability offered by a particular image. A methodology to 
                         organize thematic classes into scenarios is proposed in this 
                         study. The maximum likelihood classifier (MLC), with specific 
                         distributions for SAR data, and support vector machine were 
                         considered in this study. The iterated conditional modes algorithm 
                         was adopted to incorporate the contextual information in both 
                         methods. Considering a kappa coefficient equal to 0.8 as an 
                         acceptable minimum, the experiments show that none subset of 
                         polarization or fully polarimetric image allows performing 
                         discrimination between forest and regeneration types; 
                         single-polarized HV data provide acceptable results when the 
                         classification problem deals with the discrimination of a few 
                         classes; depending on the classification scenario, the 
                         dual-polarized HH+HV image produces similar results when compared 
                         to multipolarized (i.e., HH+HV+VV) data; in turn, if the MLC 
                         method is adopted, multipolarized data may produce close or 
                         statistically indifferent classification results compared to those 
                         produced with the use of fully polarimetric data.",
                  doi = "10.1109/JSTARS.2016.2594133",
                  url = "http://dx.doi.org/10.1109/JSTARS.2016.2594133",
                 issn = "1939-1404 and 2151-1535",
                label = "lattes: 9840759640842299 2 NegriDutrFreiLu:2016:ExCaAL",
             language = "pt",
           targetfile = "negri_exploring.pdf",
        urlaccessdate = "07 maio 2024"
}