000 03180nam a22005175i 4500
001 978-3-319-15741-2
003 DE-He213
005 20200421112219.0
007 cr nn 008mamaa
008 150425s2015 gw | s |||| 0|eng d
020 _a9783319157412
_9978-3-319-15741-2
024 7 _a10.1007/978-3-319-15741-2
_2doi
050 4 _aGA102.4.R44
050 4 _aG70.39-70.6
072 7 _aRGW
_2bicssc
072 7 _aTEC036000
_2bisacsh
082 0 4 _a910.285
_223
100 1 _aNunes Kehl, Thiago.
_eauthor.
245 1 0 _aReal time deforestation detection using ANN and Satellite images
_h[electronic resource] :
_bThe Amazon Rainforest study case /
_cby Thiago Nunes Kehl, Viviane Todt, Maur�icio Roberto Veronez, Silvio Cesar Cazella.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aX, 67 p. 25 illus., 21 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _a1 Introduction -- 2 Literature Review -- 3 Method -- 4 Results and Discussion -- 5 Conclusions and Future Work.
520 _aThe 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.
650 0 _aGeography.
650 0 _aArtificial intelligence.
650 0 _aRemote sensing.
650 1 4 _aGeography.
650 2 4 _aRemote Sensing/Photogrammetry.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aTodt, Viviane.
_eauthor.
700 1 _aRoberto Veronez, Maur�icio.
_eauthor.
700 1 _aCesar Cazella, Silvio.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319157405
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-15741-2
912 _aZDB-2-SCS
942 _cEBK
999 _c57297
_d57297