Data/hora: 16/06/2024 10:24:02

Provedor de dados: 	70	
País: 	Brazil	
Título: 	COMPARATIVE ASSESSMENT BETWEEN PER-PIXEL AND OBJECT-ORIENTED FOR MAPPING LAND COVER AND USE	
Autores: 	Prudente,Victor H. R.; Silva,Bruno B. da; Johann,Jerry A.; Mercante,Erivelto; Oldoni,Lucas V.	
Data: 	2017-09-01	
Ano: 	2017	
Palavras-chave: 	GeoDMA; Data mining; Decision tree.	
Resumo: 	ABSTRACT: The traditional per-pixel classification methods consider only spectral information, and may be limited. Object-based classifiers, however, also consider shape and texture, firstly segmenting the image, and then classifying individual objects. Thus, a Geographic Object-Based Image Analysis (GEOBIA) was compared in conjunction with data mining techniques and a traditional per-pixel method. A cut of Landsat-8, bands 2 to 7, orbit/point 223/77, located between the municipalities of Cascavel, Corbélia, Cafelândia and Tupãssi, in the west part of the state of Paraná, from 12/18/2013 was used. In the GEOBIA approach was realized image segmentation, spatial and spectral attribute extraction, and classification using the decision tree supervised algorithm, J48. For the per-pixel method, we used the supervised Maximum Likelihood Classifier. Both approaches presented equivalent results, with Kappa Index of 0.75 and Global Accuracy (GA) of 78.97% for the approach by GEOBIA and Kappa Index of 0.72 and GA of 77.44% for the perpixel classification. The classification by GEOBIA showed better accuracy for the soil, forest and soybean classes, and did not show the splash aspect, which visually improves the classification result.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000501015	
Editor: 	Associação Brasileira de Engenharia Agrícola	
Relação: 	10.1590/1809-4430-eng.agric.v37n5p1015-1027/2017	
Formato: 	text/html	
Fonte: 	Engenharia Agrícola  v.37 n.5 2017	
Fonte: 	info:eu-repo/semantics/openAccess