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@Article{PereiraFreiSantReis:2018:EvOpRa,
               author = "Pereira, Luciana O. and Freitas, Corina C. and Sant'Anna, Sidnei 
                         Jo{\~a}o Siqueira and Reis, Mariane Souza",
          affiliation = "{University of Exeter} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Evaluation of Optical and Radar Images Integration Methods for 
                         LULC Classification in Amazon Region",
              journal = "IEEE Journal of Selected Topics in Applied Earth Observations and 
                         Remote Sensing",
                 year = "2018",
               volume = "11",
               number = "9",
                pages = "3062--3074",
                month = "sept.",
             keywords = "—Brazilian Amazon, data integration, land-use and land-cover 
                         (LULC), multipolarized-synthetic aperture radar (SAR).",
             abstract = "The main objective of this study is to evaluate different methods 
                         to integrate (fusion and combination) Synthetic Aperture Radar 
                         (SAR) Advanced Land Observing Satellite (ALOS) Phased Arrayed 
                         L-band SAR (PALSAR-1) (Fine Beam Dual mode-FDB) and LANDSAT images 
                         in order to identify those which lead to higher accuracy of 
                         land-use and land-cover (LULC) mapping in an agricultural frontier 
                         region in Amazon. One method used to integrate the multipolarized 
                         information in SAR images before the fusion process was also 
                         evaluated. In this method, the first principal component (PC1 ) of 
                         SAR data was used. Color compositions of fused data that presented 
                         better LULC classification were visually analyzed. Considering the 
                         proposed objective, the following fusion methods must be 
                         highlighted: Ehlers, Wavelet a´ trous, Intensity, Hue and 
                         Saturation (IHS), and selective principal component analysis 
                         (SPC). These latter three methods presented good results when 
                         processed using PC1 from ALOS/PALSAR-1 FBD backscatter filtered 
                         image or three SAR extracted and selected features. These results 
                         corroborate with the applicability of the proposed method for SAR 
                         data information integration. Distinct methods better discriminate 
                         different LULC classes. In general, densely forested classes were 
                         better characterized by the Ehlers_TM6 fusion method, in which at 
                         least the polarization HV was used. Intermediate and initial 
                         regeneration classes were better discriminated using SPC-fused 
                         data with PC1 of ALOS/PALSAR1 FBD data. Bare soil and pasture 
                         classes were better discriminated in optical features and the PC1 
                         of ALOS/PALSAR-1 FBD data fused by the IHS method. Soybean with 
                         approximately 40 days from seeding was better discriminated in 
                         image classification obtained from ALOS/PALSAR-1 FBD image.",
                  doi = "10.1109/JSTARS.2018.2853647",
                  url = "http://dx.doi.org/10.1109/JSTARS.2018.2853647",
                 issn = "1939-1404 and 2151-1535",
             language = "en",
           targetfile = "pereira_evaluation.pdf",
        urlaccessdate = "25 nov. 2020"
}


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