284.

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Chapter 2
Pattern recognition principles

In the context of pattern recognition, a pattern is a vector of features describing an object. This pattern is made up of measurements on a set of features, which can be thought of as the axes of a k-dimensional space, called the feature space. The aim of pattern recognition is to establish a relationship between a pattern and a class label. The relationship between the object and the class label may be one-to-one (producing a hard classification) or one-to-many (producing a fuzzy classification). The features describing the object may be spectral reflectance or emittence values from optical or infrared imagery, radar backscatter values, secondary measurements derived from the image (such as texture), or geographical features such as terrain elevation, slope and aspect. The object may be a single pixel or a set of adjacent pixels forming a geographical entity, such as an agricultural field. Finally, the class labels may be known or unknown in the sense that the investigator may, in the case of a known label set, be able to list all of the categories present in the area of study. In other cases, the investigator may wish to determine the number of separable categories and their location and extent. Using this information, the separable classes are assigned labels or names based on the investigator’s knowledge of the geographical characteristics of the area of study.

These two methods of labelling are known as the supervised and unsupervised approaches, though some approaches to pattern recognition use a combination of both. Supervised methods require the user to collect samples to ‘train’ or teach the classifier to determine the decision boundaries in feature space, and such decision boundaries are significantly affected by the properties and the size of the samples used to train the classifier. For instance, if one decides to use the minimum distance between a pixel and the mean of each class as the classification criterion, one has first to collect samples to construct estimates of the class means. The acceptability of the results will depend on how adequately these class means are estimated.

The label set selected for supervised classification experiments identify information classes. The investigator should have sufficient knowledge of

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