Abstract
Accurate global land cover information is required for many aspects of global change research. Remote sensing provides the only viable basis for the production of this information. This paper reports research undertaken as part of the MODIS effort to map the land cover of North America using the ARTMAP neural network. The main objective is to design a system called ART-VIP (ART for Visualisation and Image Processing) that integrates the ARTMAP neural network algorithm into a standard public domain image processing software, and to help users analyse and interpret the dynamics of the ARTMAP neural network with scientific visualisation tools. The provision of public domain software and methodologies facilitates the use of the ARTMAP neural network architecture for other land cover classification problems.
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Liu, W., Gopal, S., Woodcock, C. (2001). Spatial Data Mining for Classification, Visualisation and Interpretation with Artmap Neural Network. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_12
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DOI: https://doi.org/10.1007/978-1-4615-1733-7_12
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