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@article{1324278, author = {Kýnová, Andrea and Dobrovolný, Petr}, article_number = {2}, doi = {http://dx.doi.org/10.14712/23361980.2015.94}, keywords = {image classification; multilayer perceptron; urban land cover; ASTER}, language = {eng}, issn = {0300-5402}, journal = {Acta Universitatis Carolinae Geographica}, title = {A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA).}, url = {http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56}, volume = {50}, year = {2015} }
TY - JOUR ID - 1324278 AU - Kýnová, Andrea - Dobrovolný, Petr PY - 2015 TI - A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA). JF - Acta Universitatis Carolinae Geographica VL - 50 IS - 2 SP - 153-163 EP - 153-163 SN - 03005402 KW - image classification KW - multilayer perceptron KW - urban land cover KW - ASTER UR - http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56 L2 - http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56 N2 - 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. ER -
KÝNOVÁ, Andrea a Petr DOBROVOLNÝ. A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA). \textit{Acta Universitatis Carolinae Geographica}. 2015, roč.~50, č.~2, s.~153-163. ISSN~0300-5402. Dostupné z: https://dx.doi.org/10.14712/23361980.2015.94.
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