Studying the behaviour of neural and statistical classifiers by interaction in feature space
Unsupervised as well as supervised classification of multi-spectral remote sensing data can be done by statistical as well as by neural network classifiers. A software tool is used to study training and classification processes in n-dimensional feature space. This tool allows visualisation of the data points in feature space, of the individually classified clusters and of the decision boundaries of the classifier. Image sequences are used to visualise higher-dimensional feature spaces as well as dynamic processes such as the training of neural networks or the effect of this training on an image to be classified. Finally, an example is given of a combined classification scheme where visualisation is used in order to validate the approach.
Bibliographic Reference: Paper presented: EUROPTO, Roma (IT), September 26-30, 1994
Availability: Available from (1) as Paper EN 38574 ORA
Record Number: 199510431 / Last updated on: 1995-04-11
Original language: en
Available languages: en