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		<isbn>978-85-17-00059-1</isbn>
		<citationkey>SousaSouzZaneCarv:2012:AnRaRe</citationkey>
		<title>Analysis of RapidEye´s red edge band for image segmentation and classification</title>
		<format>On-line.</format>
		<year>2012</year>
		<secondarytype>PRE CI</secondarytype>
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		<author>Sousa, Célio Helder Resende,</author>
		<author>Souza, Carolina Gusmão,</author>
		<author>Zanella, Lisiane,</author>
		<author>Carvalho, Luis Marcelo Tavares de,</author>
		<electronicmailaddress>celiohelder@gmail.com</electronicmailaddress>
		<electronicmailaddress>carolinagusmaosouza@gmail.com</electronicmailaddress>
		<electronicmailaddress>lisianezanella@gmail.com</electronicmailaddress>
		<electronicmailaddress>passarinho@dcf.ufla.br</electronicmailaddress>
		<editor>Feitosa, Raul Queiroz,</editor>
		<editor>Costa, Gilson Alexandre Ostwald Pedro da,</editor>
		<editor>Almeida, Cláudia Maria de,</editor>
		<editor>Fonseca, Leila Maria Garcia,</editor>
		<editor>Kux, Hermann Johann Heinrich,</editor>
		<e-mailaddress>wanderf@dsr.inpe.br</e-mailaddress>
		<conferencename>International Conference on Geographic Object-Based Image Analysis, 4 (GEOBIA).</conferencename>
		<conferencelocation>Rio de Janeiro</conferencelocation>
		<date>May 7-9, 2012</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>518-523</pages>
		<booktitle>Proceedings</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>Multiresolution Segmentation, Land cover, Decision Tree, Accuracy, Attributes.</keywords>
		<abstract>The objective of this study was to evaluate if a multi-resolution segmentation algorithm is sensitive to the RapidEyes Red Edge band and its benefits for vegetation mapping using GEOBIA and machine learning. We used a high-resolution multi-spectral RapidEye image taken in June, 2010. This image was segmented with a multiresolution segmentation algorithm (MRIS) using a fine scale parameter (300) and thirteen different weights (from 0 to 100) were assigned to the Red Edge spectral band to evaluate its influence in the segmentation and classification process. Each weight generated a segmented image. Attributes related to spectral information, geometry and texture were calculated for each image segment using the eCognition Developer®. Visual interpretation was performed along with field data to select seven classes (Dense vegetation, Meadow, Mining area, Bare land, Rock outcrop, Urban area and Water). A sample of 800 objects described by its attributes was selected from each segmented image. A decision tree approach based on CART was applied to the samples to select the attributes that provides the best separation among the classes within the scene. An accuracy assessment for the classification using CART was performed to compare the different weights assigned to the Red edge spectral band. Results showed that the Red Edge channel had no significant influence on the segmentation process. The attributes importance rank showed that the index derived from Red Edge channel can be used as input for image classification.</abstract>
		<area>SRE</area>
		<subject>Segmentation</subject>
		<session>Segmentation</session>
		<type>Segmentation</type>
		<language>en</language>
		<targetfile>137.pdf</targetfile>
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