<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>marte2.sid.inpe.br 802</site>
		<identifier>8JMKD3MGP6W34M/3U257BE</identifier>
		<repository>sid.inpe.br/marte2/2019/09.06.17.17</repository>
		<lastupdate>2019:09.06.17.17.52 dpi.inpe.br/marte2/2013/05.17.15.03.06 administrator</lastupdate>
		<metadatarepository>sid.inpe.br/marte2/2019/09.06.17.17.53</metadatarepository>
		<metadatalastupdate>2019:12.13.01.17.16 dpi.inpe.br/marte2/2013/05.17.15.03.06 administrator {D 2019}</metadatalastupdate>
		<isbn>978-85-17-00097-3</isbn>
		<citationkey>FerreiraCuéHapTheFei:2019:MaEuPl</citationkey>
		<title>Mapping eucalyptus plantations and natural forest areas in Landsat-TM images using deep learning</title>
		<format>Internet</format>
		<year>2019</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>1980 KiB</size>
		<author>Ferreira, Matheus Pinheiro,</author>
		<author>Cué La Rosa, Laura Elena,</author>
		<author>Happ, Patrick Nigri,</author>
		<author>Theobald, Raissa Brand,</author>
		<author>Feitosa, Raul Queroz,</author>
		<affiliation>Instituto Militar de Engenharia (IME)</affiliation>
		<affiliation>Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)</affiliation>
		<affiliation>Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)</affiliation>
		<affiliation>Instituto Militar de Engenharia (IME)</affiliation>
		<affiliation>Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)</affiliation>
		<electronicmailaddress>matheus@ime.eb.br</electronicmailaddress>
		<electronicmailaddress>lauracue@ele-puc-rio.br</electronicmailaddress>
		<electronicmailaddress>patrick@ele.puc-rio.br</electronicmailaddress>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress>raul@ele.puc-rio.br</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Sanches, Ieda DelArco,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 19 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>14-17 abril 2019</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>2650-2653</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>full paper</tertiarytype>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>Convolutional Neural Networks, patchclassification, random forest, satellite images, tropical forests.</keywords>
		<abstract>Automatic mapping of planted and natural forests using satellite images is a challenging task due to spectral similarity issues. In this work, we assessed the use of Convolutional Neural Networks (CNNs) to discriminate between natural forest areas and eucalyptus plantations in a Landsat-TM scene. First, we produced training and testing datasets with data from the MapBiomas project. Then, CNNs were trained with input patches of different sizes (55, 77, 9  9 and 11  11 pixels) to evaluate the influence of patch dimension in the classification accuracy. For comparison, pixel-wise and patch-classification were performed using the Random Forest (RF) algorithm. The best results were obtained using CNNs with 5  5 patches. In this scenario, the F-score was of 97.64% for natural forests and 95.49% for eucalyptus plantations. The classification errors reached 9.06% using RF and did not exceed 3% with CNNs.</abstract>
		<area>SRE</area>
		<type>Floresta e outros tipos de vegetação</type>
		<language>pt</language>
		<targetfile>97365.pdf</targetfile>
		<usergroup>simone</usergroup>
		<visibility>shown</visibility>
		<copyright>urlib.net/www/2012/11.12.15.19</copyright>
		<rightsholder>originalauthor yes</rightsholder>
		<documentstage>not transferred</documentstage>
		<mirrorrepository>urlib.net/www/2011/03.29.20.55</mirrorrepository>
		<nexthigherunit>8JMKD3MGP6W34M/3UCAT7H</nexthigherunit>
		<citingitemlist>sid.inpe.br/marte2/2019/11.08.12.52 2</citingitemlist>
		<citingitemlist>sid.inpe.br/marte2/2023/05.18.17.15 1</citingitemlist>
		<hostcollection>dpi.inpe.br/marte2/2013/05.17.15.03.06</hostcollection>
		<username>simone</username>
		<lasthostcollection>dpi.inpe.br/marte2/2013/05.17.15.03.06</lasthostcollection>
		<url>http://marte2.sid.inpe.br/rep-/sid.inpe.br/marte2/2019/09.06.17.17</url>
	</metadata>
</metadatalist>