  Supervised statistical classification is a vital tool for satellite image
processing. It is useful not only when a discrete result, such as feature
extraction or surface type, is required, but also for continuum retrievals by
dividing the quantity of interest into discrete ranges. Because of the high
resolution of modern satellite instruments and because of the requirement for
real-time processing, any algorithm has to be fast to be useful. Here we
describe an algorithm based on kernel estimation called Adaptive Gaussian
Filtering that incorporates several innovations to produce superior efficiency
as compared to three other popular methods: k-nearest-neighbour (KNN), Learning
Vector Quantization (LVQ) and Support Vector Machines (SVM). This efficiency is
gained with no compromises: accuracy is maintained, while estimates of the
conditional probabilities are returned. These are useful not only to gauge the
accuracy of an estimate in the absence of its true value, but also to
re-calibrate a retrieved image and as a proxy for a discretized continuum
variable. The algorithm is demonstrated and compared with the other three on a
pair of synthetic test classes and to map the waterways of the Netherlands.
Software may be found at: http://libagf.sourceforge.net.
