@Article{SilvaSouzFerrQuei:2022:SpSeSa,
               author = "Silva, Baggio Luiz de Castro e and Souza, Felipe Carvalho de and 
                         Ferreira, Karine Reis and Queiroz, Gilberto Ribeiro de",
          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)}",
                title = "Spatiotemporal segmentation of satellite image time series using 
                         self-organizing map",
              journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
                         Information Sciences",
                 year = "2022",
               volume = "3",
                pages = "255--261",
             keywords = "Spatiotemporal Segmentation, Satellite Image Time Series, Land Use 
                         and Land Cover Changes, Unsupervised Classification, Clustering 
                         Algorithms, Earth Data Cubes.",
             abstract = "Nowadays, researchers have free access to an unprecedentedly large 
                         amount of remote sensing images collected by satellites and 
                         sensors with different spatial, temporal, and spectral 
                         resolutions. This scenario has promoted the use of satellite image 
                         time series for spatiotemporal analysis. This paper presents a 
                         methodology for spatiotemporal segmentation of satellite image 
                         time series. Spatiotemporal segmentation finds homogeneous regions 
                         in space and time from remote sensing images based on spectral 
                         features. The proposed approach is unsupervised based on the 
                         self-organizing map (SOM) neural network and hierarchical 
                         clustering algorithm. It was implemented and applied to a region 
                         in the Mato Grosso state, Brazil. The results were evaluated using 
                         qualitative and quantitative approaches. In the qualitative 
                         approach, visual analysis was performed based on the land use and 
                         land cover map of the TerrraClass Cerrado project. In the 
                         quantitative approach, supervised and geometric metrics were used 
                         to analyze the quality of the produced segments. The results 
                         obtained are promising since the segments produced were 
                         homogeneous and with a low occurrence of over-segmentation.",
                  doi = "10.5194/isprs-annals-V-3-2022-255-2022",
                  url = "http://dx.doi.org/10.5194/isprs-annals-V-3-2022-255-2022",
                 issn = "0924-2716",
                label = "lattes: 4816443925174561 1 SilvaFerrQueiSant:2022:SPSESA",
             language = "en",
           targetfile = "isprs-annals-V-3-2022-255-2022.pdf",
                  url = "https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/255/2022/",
        urlaccessdate = "20 maio 2024"
}