Real time deforestation detection using ANN and Satellite images (Record no. 57297)
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| 000 -LEADER | |
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| fixed length control field | 03180nam a22005175i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-15741-2 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421112219.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150425s2015 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319157412 |
| -- | 978-3-319-15741-2 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 910.285 |
| 100 1# - AUTHOR NAME | |
| Author | Nunes Kehl, Thiago. |
| 245 10 - TITLE STATEMENT | |
| Title | Real time deforestation detection using ANN and Satellite images |
| Sub Title | The Amazon Rainforest study case / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | X, 67 p. 25 illus., 21 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Computer Science, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 1 Introduction -- 2 Literature Review -- 3 Method -- 4 Results and Discussion -- 5 Conclusions and Future Work. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Todt, Viviane. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Roberto Veronez, Maur�icio. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Cesar Cazella, Silvio. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-15741-2 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2015. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Geography. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Remote sensing. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Geography. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Remote Sensing/Photogrammetry. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-5768 |
| 912 ## - | |
| -- | ZDB-2-SCS |
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