<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified.</p><p>The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units.</p></abstract>

Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified.The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units.

Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend

Department of Geoinformatics, Cartography and Remote Sensing Faculty of Geography and Regional Studies, University of Warsaw

Remote Sensing Centre, Institute of Geodesy and Cartography, Warsaw

Polish Cartographical Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key...

Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend

Data publikacji: 06 maj 2016

Zakres stron: 203 - 212

DOI: https://doi.org/10.1515/pcr-2015-0018

Słowa kluczowe
classification, Corine Land Cover, Landsat, artificial neural networks, Warsaw

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend

Data publikacji: 06 maj 2016

Zakres stron: 203 - 212

DOI: https://doi.org/10.1515/pcr-2015-0018

Słowa kluczoweclassification, Corine Land Cover, Landsat, artificial neural networks, Warsaw

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
classification, Corine Land Cover, Landsat, artificial neural networks, Warsaw