Open AccessApplication Of Self-Organizing Neural Networks For The Delineation of Excess Water Areas
Author(s) -
Gábor Szántó,
László Mucsi,
Boudewijn van Leeuwen
Publication year - 2008
Publication title -
journal of environmental geography
Language(s) - English
Resource type - Journals
eISSN - 2060-467X
pISSN - 2060-3274
DOI - 10.14232/jengeo-2008-43860
Subject(s) - artificial neural network , self organizing map , pixel , thematic map , multispectral pattern recognition , multispectral image , computer science , set (abstract data type) , homogeneous , pattern recognition (psychology) , artificial intelligence , data set , satellite , remote sensing , thematic mapper , data mining , cartography , satellite imagery , geography , mathematics , combinatorics , aerospace engineering , engineering , programming language
In recent times Artificial Neural Networks (ANNs) are more and more widely applied. The ANN is an information processing system consisting of numerous simple processing units (neurons) that are arranged in layers and have weighted connections to each other. In the present study the possible application of an unsupervised neural network model, the self-organizing map (SOM), for the delineation of excess water areas have been examined. By means of the self-organizing map high-dimensional data of large databases could be mapped to a low-dimensional data space. Within a data set, it is able to develop homogeneous clusters, thus it can be effectively applied for the classification of multispectral satellite images. The classification was carried out for an area of 88 km2 to the south of Hódmezővásárhely situated in the south-eastern part of Hungary, which is frequently inundated by excess water. As input data, the intensity values of the pixels measured in six bands of a Landsat ETM image taken on 23rd April 2000 were used. To perform the classification, three different sized neural network models were created, which classified the pixels of the satellite image to 9, 12 and 16 clusters. By using the gained clusters three thematic maps were created, on which different types of excess water areas were delineated. During the validation of the results it was concluded that the applied neural network model is suitable for the delimitation of excess water areas and it could be an alternative to the traditional classification methods.