Neural Networks Applied To The Classification Of Remotely Sensed Data

Abstract
A neural network with topology 2-8-8 is evaluated against the standard of supervised non-parametric maximum likelihood classification. The purpose of the evaluation is to compare the performance in terms of training speed and quality of classification. Classification is done on multispectral data from the Thematic Mapper(TM3,TM4) in combination with a ground reference class map. This type of data is familiar to professionals in the field of remote sensing. This means that the position of clusters in feature space is well known and understood, and that the spatial pattern is equally well known. As a spin-off, the application of a neural net to a classical task of statistical pattern recognition helps to demystify neurai networks