Pattern recognition in urban areas is one of the most challenging issues in
classifying satellite remote sensing data. Parametric pixel-by-pixel classification
algorithms tend to perform poorly in this context. This is because urban areas
comprise a complex spatial assemblage of disparate land cover types - including
built structures, numerous vegetation types, bare soil and water bodies. Thus,
there is a need for more powerful spectral pattern recognition techniques,
utilizing pixel-by-pixel spectral information as the basis for automated urban
land cover detection. This paper adopts the multi-layer perceptron classifier
suggested and implemented in [5]. The objective of this study is to analyse the
performance and stability of this classifier - trained and tested for supervised
classification (8 a priori given land use classes) of a Landsat-5 TM image
(270 x 360 pixels) from the city of Vienna and its northern surroundings
- along with varying the training data set in the single-training-site case.
The performance is measured in terms of total classification, map user's and
map producer's accuracies. In addition, the stability with initial parameter
conditions, classification error matrices, and error curves are analysed in some
detail. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc
