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		<isbn>978-1-61208-871-6</isbn>
		<issn>2308-393X</issn>
		<citationkey>PachecoMaSiSoShEs:2021:ImClMe</citationkey>
		<title>Image Classification Methods Assessment for Identification of Small-Scale Agriculture in Brazilian Amazon</title>
		<shorttitle>Slides</shorttitle>
		<format>On-line</format>
		<year>2021</year>
		<secondarytype>PRE CI</secondarytype>
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		<author>Pacheco, Flávia Domingos,</author>
		<author>Matias, Maíra Ramalho,</author>
		<author>Silva, Gabriel Máximo da,</author>
		<author>Souza, Anielli Rosane de,</author>
		<author>Shimabukuro, Yosio Edemir,</author>
		<author>Escada, Maria Isabel Sobral,</author>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>flavia.pacheco@inpe.br</electronicmailaddress>
		<electronicmailaddress>mairamatias.geo@gmail.com</electronicmailaddress>
		<electronicmailaddress>gabrielmaximo04@gmail.com</electronicmailaddress>
		<electronicmailaddress>aniellirosane@yahoo.com.br</electronicmailaddress>
		<electronicmailaddress>edemirshima@gmail.com</electronicmailaddress>
		<electronicmailaddress>isabel.escada@inpe.br</electronicmailaddress>
		<conferencename>International Conference on Advanced Geographic Information Systems, Applications, and Services, 13 (GEOProcessing)</conferencename>
		<conferencelocation>Nice, France</conferencelocation>
		<date>19-22 july</date>
		<publisher>IARIA</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
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		<keywords>digital image processing, segmentation, land use, land cover, smallholders, planetscope.</keywords>
		<abstract>This paper aims to test different methods for image classification focusing on small-scale agriculture in the region of Mocajuba and Cametá, municipalities in the Northeast of Pará state, Brazil. It is an important land use class, always ignored by Land-Use and Land-Cover monitoring systems because of its small size and variable spectral signature. We used an image from the PlanetScope Surface Reflectance Mosaics (Analysis Ready) with spatial resolution of 4.77 meters and 4 spectral bands (red, green, blue and infra-red). After proceeding with a multiresolution segmentation to identify image objects, two object-oriented classification algorithms were tested: Adapted Nearest-neighbor and C5.0 Decision trees algorithms. We selected 122 random points using the images available on Google Earth Pro as reference to assess the accuracy of classifications. Afterwards, confusion matrices were generated. Both methods showed similar overall accuracy and kappa value. However, C5.0 Decision trees reached a higher producers accuracy to small-scale agriculture (75%) in comparison to Adapted Nearest-neighbor (65%). The average size of the small-scale agriculture segments estimated was less than 1 ha in both maps, showing the need to carry out studies on scales of greater detail, preferably with images of high spatial resolution to represent these systems properly. In this study, C5.0 Decision trees had the best result, representing the most suitable method for mapping small-scale agriculture in Brazilian Amazon.</abstract>
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