Studying the behavior of neural and statistical classifiers by interaction in feature space

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Abstract
Unsupervised as well as classification of multi-spectral remote sensing data can be done by statistical as well as by neural network classifiers. Since classifiers are often approached as black boxes, it is not clear why one particular classifier performs better for a certain problem than another. In order to gain some insight in the actual training and classification processes, we implemented a software tool to study these processes in n-dimensional feature space. This tool allows visualization 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 visualize 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. The visualization approach was further extended by allowing interaction with the decision boundaries. Feedback of this interaction is provided by a direct link between the decision boundaries and the classified landuse image. Pushing or pulling a decision boundary is directly reflected by changes to the corresponding classified image. Finally, we give an example of a combined classification scheme where visualization is used in order to validate the approach.

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