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@Article{FreitasHaerShim:2008:LiSpMi,
               author = "Freitas, Ramon Morais de and Haertel, V. b and Shimabukuro, Yosio 
                         Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Universidade Federal do Rio Grande do Sul, Centro de Sensoriamento 
                         Remoto e Meteorologia, Caixa Postal - 15044, Porto Alegre, RS, 
                         Brazil and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Linear spectral mixture model in moderate spatial resolution image 
                         data / Modelo linear de mistura espectral em imagem de moderada 
                         resolu{\c{c}}{\~a}o espacial",
              journal = "Boletim de Ciencias Geodesicas",
                 year = "2008",
               volume = "14",
               number = "17",
                pages = "55--71",
                month = "Jan.",
             keywords = "GEOBASE Subject Index: data set, image analysis, linearity, pixel, 
                         radiometric method, remote sensing, spatial resolution, spectral 
                         reflectance, CBERS-2.",
             abstract = "The concept of spectral mixture offers a wide range of 
                         applications in the Remote Sensing area. The application of this 
                         concept, however, requires the prior estimation of the component's 
                         (endmembers) spectral response. This latter requirement can be 
                         achieved by different methods, as reported in the literature, such 
                         as techniques for the detection of pure pixels, use of spectral 
                         libraries, and field radiometric measurements. Among those, the 
                         most often used is the pure pixel approach. In this approach, the 
                         components' spectral reflectances are estimated by means of pixels 
                         covered entirely by a single component. This approach offers the 
                         advantage of allowing the extraction of the required spectral 
                         reflectance directly from the image data. This approach, however, 
                         becomes increasingly unfeasible as the spatial resolution of the 
                         image data decreases, due to the larger ground area covered by a 
                         single pixel. In this study we propose a methodology to estimate 
                         the spectral reflectance for each component class in moderate 
                         spatial resolution image data, by applying the linear mixing model 
                         (MLME), and higher spatial resolution image data as auxiliary 
                         data. It is expected that this methodology will provide a more 
                         practical way to implement the spectral mixture approach to 
                         moderate resolution image data, allowing in this way the expansion 
                         of the information about the components' proportions across larger 
                         areas, up-scaling information in regional and global studies. 
                         Experiments were carried out using CCD (20 m ground resolution) 
                         and IRMSS (80 m ground resolution) and WFI (260 m ground 
                         resolution) CBERS-2 image data, as medium and moderate spatial 
                         resolution data, respectively. The spectral reflectances for the 
                         components in the IRMSS and WFI CBERS-2 spectral bands are 
                         estimated by applying the proposed methodology. The reliability of 
                         the proposed methodology was assessed by both analyzing scatter 
                         plots for CBERS-2 data and by comparing the fraction images 
                         produced by image data sets of the sensors analyzed.",
                  doi = "Marciana",
                  url = "http://dx.doi.org/Marciana",
                label = "self-archiving-INPE-MCTI-GOV-BR",
             language = "en",
        urlaccessdate = "14 ago. 2020"
}


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