@Article{SantosFerCamPicSim:2021:QuCoCl,
               author = "Santos, Lorena Alves dos and Ferreira, Karine Reis and Camara, 
                         Gilberto and Picoli, Michelle Cristina Ara{\'u}jo and 
                         Sim{\~o}es, Rolf Ezequiel de Oliveira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Quality control and class noise reduction of satellite image time 
                         series",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2021",
               volume = "177",
                pages = "75--88",
                month = "July",
             keywords = "Bayesian inference, Class noise reduction, Land use and cover 
                         classification, Satellite image time series, Self-organizing 
                         map.",
             abstract = "The extensive amount of Earth observation satellite images 
                         available brings opportunities and challenges for land mapping in 
                         global and regional scales. These large datasets have motivated 
                         the use of satellite image time series analysis coupled with 
                         machine learning techniques to produce land use and cover class 
                         maps. To be successful, these methods need good quality training 
                         samples, which are the most important factor for determining the 
                         accuracy of the results. For this reason, training samples need 
                         methods for quality control of class noise. In this paper, we 
                         propose a method to assess and improve the quality of satellite 
                         image time series training data. The method uses self-organizing 
                         maps (SOM) to produce clusters of time series and Bayesian 
                         inference to assess intra-cluster and inter-cluster similarity. 
                         Consistent samples of a class will be part of a neighborhood of 
                         clusters in the SOM map. Noisy samples will appear as outliers in 
                         the SOM. Using Bayesian inference in the SOM neighborhoods, we can 
                         infer which samples are noisy. To illustrate the methods, we 
                         present a case study in a large training set of land use and cover 
                         classes in the Cerrado biome, Brazil. The results prove that the 
                         method is efficient to reduce class noise and to assess the 
                         spatio-temporal variation of satellite image time series training 
                         samples.",
                  doi = "10.1016/j.isprsjprs.2021.04.014",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2021.04.014",
                 issn = "0924-2716",
             language = "en",
           targetfile = "santos_quality.pdf",
        urlaccessdate = "12 jun. 2024"
}