@Article{VieiraQueiShig:2022:AnInNu,
               author = "Vieira, Leonardo de Souza and Queiroz, Gilberto Ribeiro de and 
                         Shiguemori, Elcio H.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto de Estudos 
                         Avan{\c{c}}ados (IEAv)}",
                title = "An analysis of the influence of the number of observations in a 
                         random forest time series classification to map the forest and 
                         deforestation in the brazilian Amazon",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2022",
               volume = "43",
               number = "B3",
                pages = "721--728",
                month = "June",
             keywords = "Brazilian Amazon, Classification, Land cover, Landsat, Random 
                         Forest, Time series.",
             abstract = "Remote sensing has been an essential tool in combating 
                         deforestation. However, the ever-rising deforestation rates 
                         require new remote sensing techniques. This paper presents a study 
                         to determine the effects on the accuracy of the data analysis of 
                         varying the number of satellite observations, using a Random 
                         Forest classification algorithm. We carried out experiments on the 
                         Landsat-8 data cube with 22 images and developed an automatic 
                         sampling system based on PRODES to generate the labeled time 
                         series. We split the time series dataset to build data subsets 
                         with different number of observations. The results showed that a 
                         fewer number of observations negatively effects the accuracy of 
                         the RF algorithm when analyzing deforested areas, but not forest 
                         areas. The RF classifiers were compared using a random test data 
                         set, where all classifiers presented an Overall Accuracy (OA), 
                         Balanced Accuracy (BA), and f1-score (F1) above 97%. In the first 
                         evaluation, the variation in the number of observations appears to 
                         cause little influence on the classification accuracy. The 
                         analysis used the reference map to contrast the RF classifier's 
                         results. The results showed that the best results in OA occurred 
                         with fewer observations. The best performance of 96% happened with 
                         four observations. We evaluated the performance of the classes, 
                         deforestation, and forest individually. The results showed that a 
                         fewer number of observations had negative effects on the accuracy 
                         of the RF algorithm when analyzing deforested areas, but not 
                         forest areas. Finally, we evaluated the visual quality of the land 
                         cover maps produced.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2022-721-2022",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2022-721-2022",
                 issn = "1682-1750",
             language = "en",
           targetfile = "isprs-archives-XLIII-B3-2022-721-2022.pdf",
        urlaccessdate = "29 jun. 2024"
}