Classification of terrain cover from satellite radar imagery represents an area of considerable
current interest and research. Most satellite sensors used for land applications are of the imaging
type. They record data in a variety of spectral channels and at a variety of ground resolutions.
Spectral pattern recognition refers to classification procedures utilizing pixel-by-pixel spectral
information as the basis for automated land cover classification. A number of methods have
been developed in the past to classify pixels [resolution cells] from multispectral imagery to a
priori given land cover categories. Their ability to provide land cover information with high
classification accuracies is significant for work where accurate and reliable thematic information
is needed. The current trend towards the use of more spectral bands on satellite instruments,
such as visible and infrared imaging spectrometers, and finer pixel and grey level resolutions
will offer more precise possibilities for accurate identification. But as the complexity of the data
grows, so too does the need for more powerful tools to analyse them.
It is the major objective of this study to analyse the capabilities and applicability of the neural
pattern recognition system, called fuzzy ARTMAP, to generate high quality classifications of
urban land cover using remotely sensed images. Fuzzy ARTMAP synthesizes fuzzy logic and
Adaptive Resonance Theory (ART) by exploiting the formal similarity between the
computations of fuzzy subsethood and the dynamics of category choice, search and learning.
The paper describes design features, system dynamics and simulation algorithms of this
learning system, which is trained and tested for classification (8 a priori given classes) of a
multispectral image of a Landsat-5 Thematic Mapper scene (270 x 360 pixels) from the City of
Vienna on a pixel-by-pixel basis. Fuzzy ARTMAP performance is compared with that of an
error-based learning system based upon the multi-layer perceptron, and the Gaussian maximum
likelihood classifier as conventional statistical benchmark on the same database. Both neural
classifiers outperform the conventional classifier in terms of classification accuracy. Fuzzy
ARTMAP leads to out-of-sample classification accuracies, very close to maximum
performance, while the multi-layer perceptron - like the conventional classifier - shows
difficulties to distinguish between some land use categories. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4163 |
| Date | 05 1900 |
| Creators | Fischer, Manfred M., Gopal, Sucharita |
| Publisher | WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/4163/ |
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