USE OF ARTIFICIAL NEURAL NETWORKS AND GEOGRAPHIC OBJECTS FOR CLASSIFYING REMOTE SENSING IMAGERY

Main Article Content

Pedro Resende Silva Fausto Weimar Acerbi Júnior Luis Marcelo Tavares de Carvalho José Roberto Soares Scolforo

Abstract

The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes. 

Article Details

How to Cite
SILVA, Pedro Resende et al. USE OF ARTIFICIAL NEURAL NETWORKS AND GEOGRAPHIC OBJECTS FOR CLASSIFYING REMOTE SENSING IMAGERY. CERNE, [S.l.], v. 20, n. 2, p. 267-276, apr. 2016. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/986>. Date accessed: 16 sep. 2019.
Keywords
Image segmentation, object-based classification, time series.
Section
Article