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Real time deforestation detection using ANN and Satellite images [electronic resource] : The Amazon Rainforest study case / by Thiago Nunes Kehl, Viviane Todt, Maur cio Roberto Veronez, Silvio Cesar Cazella.

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dc.contributor.author Nunes Kehl, Thiago. author.
dc.contributor.author Todt, Viviane. author.
dc.contributor.author Roberto Veronez, Maur cio. author.
dc.contributor.author Cesar Cazella, Silvio. author.
dc.contributor.author SpringerLink (Online service)
dc.date.accessioned 2017-11-30T21:39:44Z
dc.date.available 2017-11-30T21:39:44Z
dc.date.created 2015.
dc.date.issued 2015
dc.identifier.isbn 9783319157412
dc.identifier.uri http://dspace.conacyt.gov.py/xmlui/handle/123456789/13080
dc.description X, 67 p. 25 illus., 21 illus. in color.
dc.description.abstract The foremost aim of the present study was the development of a tool to detect daily deforestation in the Amazon rainforest, using satellite images from the MODIS/TERRA sensor and Artificial Neural Networks. The developed tool provides parameterization of the configuration for the neural network training to enable us to select the best neural architecture to address the problem. The tool makes use of confusion matrices to determine the degree of success of the network. A spectrum-temporal analysis of the study area was done on 57 images from May 20 to July 15, 2003 using the trained neural network. The analysis enabled verification of quality of the implemented neural network classification and also aided in understanding the dynamics of deforestation in the Amazon rainforest, thereby highlighting the vast potential of neural networks for image classification. However, the complex task of detection of predatory actions at the beginning, i.e., generation of consistent alarms, instead of false alarms has not been solved yet. Thus, the present article provides a theoretical basis and elaboration of practical use of neural networks and satellite images to combat illegal deforestation.
dc.description.tableofcontents 1 Introduction -- 2 Literature Review -- 3 Method -- 4 Results and Discussion -- 5 Conclusions and Future Work.
dc.language eng
dc.publisher Cham : Springer International Publishing : Imprint: Springer, 2015.
dc.relation.ispartofseries Springer eBooks
dc.relation.ispartofseries SpringerBriefs in Computer Science, 2191-5768
dc.relation.ispartofseries SpringerBriefs in Computer Science, 2191-5768
dc.relation.uri http://cicco.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-15741-2
dc.subject Geography.
dc.subject Artificial intelligence.
dc.subject Remote sensing.
dc.subject Geography.
dc.subject Remote Sensing/Photogrammetry.
dc.subject Artificial Intelligence (incl. Robotics).
dc.subject.ddc 910.285 23
dc.subject.lcc GA102.4.R44
dc.subject.lcc G70.39-70.6
dc.subject.other Computer Science (Springer-11645)
dc.title Real time deforestation detection using ANN and Satellite images [electronic resource] : The Amazon Rainforest study case / by Thiago Nunes Kehl, Viviane Todt, Maur cio Roberto Veronez, Silvio Cesar Cazella.
dc.type text
dc.identifier.doi 10.1007/978-3-319-15741-2
dc.identifier.bib 978-3-319-15741-2
dc.format.rdamedia computer
dc.format.rdacarrier online resource
dc.format.rda text file PDF


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