@InProceedings{KörtingNamiFonsFelg:2016:HoEfOb,
               author = "K{\"o}rting, Thales Sehn and Namikawa, La{\'e}rcio Massaru and 
                         Fonseca, Leila Maria Garcia and Felgueiras, Carlos Alberto",
          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 = "How to effectively obtain metadata from remote sensing big data?",
            booktitle = "Proceedings...",
                 year = "2016",
         organization = "GEOBIA 2016. Solutions and Synergies",
                 note = "{Setores de Atividade: Atividades dos servi{\c{c}}os de 
                         tecnologia da informa{\c{c}}{\~a}o.} and 
                         {Informa{\c{c}}{\~o}es Adicionais: ABSTRACT:} and What can be 
                         considered big data when dealing with remote sensing imagery? In 
                         general terms, big data is defined as data requiring high and 
                         management capabilities characterized by 3 V?s: Volume, Velocity 
                         and Variety. In the past, (e.g. 1975), considering the 
                         computational and and databases resources available, a series of 
                         Landsat-1 imagery from the same region could be considered big 
                         data. Nowadays, several and satellites are available, and they 
                         produce massive amounts of data. Certainly, an image data set 
                         obtained by a single satellite, for a and specific region and 
                         along time, fills the 3 V?s requirements to be considered big data 
                         as well. In order to deal with remote sensing big and data, we 
                         propose to explore the generation of metadata based on the 
                         detection of simple features. Besides the intrinsic geographic 
                         information on every remote sensing scene, no additional metadata 
                         is usually considered. We propose basic image processing 
                         algorithms to and detect basic well-known patterns, and include 
                         them as tags, such as cloud, shadow, stadium, vegetation, and 
                         water, according to what and is detectable at each spatial 
                         resolution. In this work we show preliminary results using imagery 
                         from RapidEye sensor, with 5 meter and spatial resolution, 
                         composed by two full coverages of Brazil with RapidEye 
                         multispectral imagery (around 40k scenes)..",
             keywords = "Big data, Remote Sensing, Metadata, Image Processing, Water 
                         indices, Pattern recognition.",
             abstract = "What can be considered big data when dealing with remote sensing 
                         imagery? In general terms, big data is defined as data requiring 
                         high management capabilities characterized by 3 Vs: Volume, 
                         Velocity and Variety. In the past, (e.g. 1975), considering the 
                         computational and databases resources available, a series of 
                         Landsat-1 imagery from the same region could be considered big 
                         data. Nowadays, several satellites are available, and they produce 
                         massive amounts of data. Certainly, an image data set obtained by 
                         a single satellite, for a specific region and along time, fills 
                         the 3 Vs requirements to be considered big data as well. In order 
                         to deal with remote sensing big data, we propose to explore the 
                         generation of metadata based on the detection of simple features. 
                         Besides the intrinsic geographic information on every remote 
                         sensing scene, no additional metadata is usually considered. We 
                         propose basic image processing algorithms to detect basic 
                         well-known patterns, and include them as tags, such as cloud, 
                         shadow, stadium, vegetation, and water, according to what is 
                         detectable at each spatial resolution. In this work we show 
                         preliminary results using imagery from RapidEye sensor, with 5 
                         meter spatial resolution, composed by two full coverages of Brazil 
                         with RapidEye multispectral imagery (around 40k scenes).",
  conference-location = "Enschede, The Nederlands",
      conference-year = "14-16 set.",
                  doi = "10.13140/RG.2.2.26048.12805",
                  url = "http://dx.doi.org/10.13140/RG.2.2.26048.12805",
                label = "lattes: 2916855460918534 4 KortingNamiFonsFelg:2016:HOEFOB",
             language = "en",
           targetfile = "korting_how.pdf",
                  url = "https://www.conftool.net/geobia2016/index.php?page=browseSessions\&abstracts=show\&form_session=16\&presentations=show",
        urlaccessdate = "03 jun. 2024"
}