000726794 000__ 03230cam\a2200493Ii\4500
000726794 001__ 726794
000726794 005__ 20230306140839.0
000726794 006__ m\\\\\o\\d\\\\\\\\
000726794 007__ cr\cn\nnnunnun
000726794 008__ 150428s2015\\\\sz\a\\\\ob\\\\001\0\eng\d
000726794 020__ $$a9783319157412$$qelectronic book
000726794 020__ $$a3319157418$$qelectronic book
000726794 020__ $$z9783319157405
000726794 0247_ $$a10.1007/978-3-319-15741-2$$2doi
000726794 035__ $$aSP(OCoLC)ocn908103334
000726794 035__ $$aSP(OCoLC)908103334
000726794 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dN$T$$dE7B$$dIDEBK$$dCOO$$dUPM$$dEBLCP$$dVLB
000726794 043__ $$asa-----
000726794 049__ $$aISEA
000726794 050_4 $$aSD418.3.A53$$bK44 2015eb
000726794 08204 $$a634.909861/6$$223
000726794 1001_ $$aKehl, Thiago Nunes,$$eauthor.
000726794 24510 $$aReal time deforestation detection using ANN and satellite images$$h[electronic resource] :$$bthe Amazon Rainforest study case /$$cThiago Nunes Kehl, Viviane Todt, Maurício Roberto Veronez, Silvio Cesar Cazella.
000726794 264_1 $$aCham :$$bSpringer,$$c2015.
000726794 300__ $$a1 online resource :$$billustrations.
000726794 336__ $$atext$$btxt$$2rdacontent
000726794 337__ $$acomputer$$bc$$2rdamedia
000726794 338__ $$aonline resource$$bcr$$2rdacarrier
000726794 4901_ $$aSpringerBriefs in computer science,$$x2191-5776
000726794 504__ $$aIncludes bibliographical references and index.
000726794 5050_ $$a1 Introduction -- 2 Literature Review -- 3 Method -- 4 Results and Discussion -- 5 Conclusions and Future Work.
000726794 506__ $$aAccess limited to authorized users.
000726794 520__ $$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.
000726794 588__ $$aOnline resource; title from PDF title page (viewed May 1, 2015).
000726794 650_0 $$aDeforestation$$zAmazon River Region$$xRemote sensing.
000726794 7001_ $$aTodt, Viviane,$$eauthor.
000726794 7001_ $$aVeronez, Maurício Roberto,$$eauthor.
000726794 7001_ $$aCazella, Silvio Cesar,$$eauthor.
000726794 77608 $$iPrint version:$$z9783319157405
000726794 830_0 $$aSpringerBriefs in computer science.
000726794 852__ $$bebk
000726794 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-15741-2$$zOnline Access$$91397441.1
000726794 909CO $$ooai:library.usi.edu:726794$$pGLOBAL_SET
000726794 980__ $$aEBOOK
000726794 980__ $$aBIB
000726794 982__ $$aEbook
000726794 983__ $$aOnline
000726794 994__ $$a92$$bISE