國立虎尾科技大學 |

Real time deforestation detection using ANN and Satellite images = The Amazon Rainforest study case /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Real time deforestation detection using ANN and Satellite images/ by Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella.
Reminder of title:	The Amazon Rainforest study case /
Author:	Nunes Kehl, Thiago.
other author:	Todt, Viviane.
Description:	X, 67 p. 25 illus., 21 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Remote sensing. -
Online resource:	https://doi.org/10.1007/978-3-319-15741-2
ISBN:	9783319157412

Real time deforestation detection using ANN and Satellite images = The Amazon Rainforest study case /
Nunes Kehl, Thiago.

Real time deforestation detection using ANN and Satellite imagesThe Amazon Rainforest study case /[electronic resource] :by Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella. - 1st ed. 2015. - X, 67 p. 25 illus., 21 illus. in color.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

1 Introduction -- 2 Literature Review -- 3 Method -- 4 Results and Discussion -- 5 Conclusions and Future Work.

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.

ISBN: 9783319157412

Standard No.: 10.1007/978-3-319-15741-2doiSubjects--Topical Terms:

557272
Remote sensing.

LC Class. No.: GA102.4.R44

Dewey Class. No.: 910.285

Real time deforestation detection using ANN and Satellite images = The Amazon Rainforest study case /
LDR:02807nam a22004095i 4500 001 963370
003 DE-He213
005 20200630004640.0
007 cr nn 008mamaa
008 201211s2015 gw | s |||| 0|eng d
020 $a 9783319157412 $9 978-3-319-15741-2
024 7 $a 10.1007/978-3-319-15741-2 $2 doi
035 $a 978-3-319-15741-2
050 4 $a GA102.4.R44
050 4 $a G70.39-70.6
072 7 $a RGW $2 bicssc
072 7 $a TEC036000 $2 bisacsh
072 7 $a RGW $2 thema
082 0 4 $a 910.285 $2 23
100 1 $a Nunes Kehl, Thiago. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258340
245 1 0 $a Real time deforestation detection using ANN and Satellite images $h [electronic resource] : $b The Amazon Rainforest study case / $c by Thiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella.
250 $a 1st ed. 2015.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a X, 67 p. 25 illus., 21 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Computer Science, $x 2191-5768
505 0 $a 1 Introduction -- 2 Literature Review -- 3 Method -- 4 Results and Discussion -- 5 Conclusions and Future Work.
520 $a 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.
650 0 $a Remote sensing. $3 557272
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Remote Sensing/Photogrammetry. $3 670396
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Todt, Viviane. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258341
700 1 $a Roberto Veronez, Maurício. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258342
700 1 $a Cesar Cazella, Silvio. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258343
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319157429
776 0 8 $i Printed edition: $z 9783319157405
830 0 $a SpringerBriefs in Computer Science, $x 2191-5768 $3 1255334
856 4 0 $u https://doi.org/10.1007/978-3-319-15741-2
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)