Publication details

A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA).

Authors

KÝNOVÁ Andrea DOBROVOLNÝ Petr

Year of publication 2015
Type Article in Periodical
Magazine / Source Acta Universitatis Carolinae Geographica
MU Faculty or unit

Faculty of Science

Citation
Web URL
Doi http://dx.doi.org/10.14712/23361980.2015.94
Field Earth magnetism, geography
Keywords image classification; multilayer perceptron; urban land cover; ASTER
Description Accurate and updated land cover maps provide crucial basic information in a number of important enterprises, with sustainable development and regional planning far from the least of them. Remote sensing is probably the most efficient approach to obtaining a land cover map. However, certain intrinsic limitations limit the accuracy of automatic approaches to image classification. Classifications within highly heterogeneous urban areas are especially challenging. This study makes a presentation of multilayer perceptron (MLP), an artificial neural network (ANN), as an applicable approach to image classification. Optimal MLP architecture parameters were established by means of a training set. The resulting network was used to classify a sub-scene within ASTER imagery. The results were evaluated against a test dataset. The overall accuracy of classification was 94.8%. This is comparable to classification results from a maximum likelihood classifier (MLC) used for the same image. In built-up areas, MLP did not exaggerate built-up areas at the expense of other classes to the same extent as MLC.
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