000			03270nam a22004455i 4500
999			_c200434334 _d52546
003			DE-He213
005			20231104114349.0
007			cr nn 008mamaa
008			150425s2015 gw | s |||| 0|eng d
020			_a9783319157412 _z978-3-319-15741-2
024	7		_a10.1007/978-3-319-15741-2 _2doi
040			_aTR-AnTOB _beng _cTR-AnTOB _erda
050		4	_aGA102.4.R44
072		7	_aRGW _2bicssc
072		7	_aTEC036000 _2bisacsh
072		7	_aRGW _2thema910.285 _223
100	1		_aNunes Kehl, Thiago. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aReal time deforestation detection using ANN and Satellite images : _bThe Amazon Rainforest study case / _cby Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_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	_aArtificial intelligence.
650	1	4	_aRemote Sensing/Photogrammetry. _0http://scigraph.springernature.com/things/product-market-codes/J13010
650	2	4	_aArtificial Intelligence. _0http://scigraph.springernature.com/things/product-market-codes/I21000
700	1		_aTodt, Viviane. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aRoberto Veronez, Maurício. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aCesar Cazella, Silvio. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-319-15741-2 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK
041			_aeng