23 research outputs found
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A one-class classifier for identifying urban areas in remotely-sensed data
For many remote sensing applications, land cover can be determined by using spectral information alone. Identifying urban areas, however, requires the use of texture information since these areas are not generally characterized by a unique spectral signature. We have designed a one-class classifier to discriminate between urban and non-urban data. The advantage to using our classification technique is that principles of both statistical and adaptive pattern recognition are used simultaneously. This prevents new data that is completely dissimilar from the training data from being incorrectly classified. At the same time it allows decision boundary adaptation to reduce classification error in overlap areas of the feature space. Results will be illustrated using a LANDSAT scene of the city of Albuquerque
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An adaptive algorithm for modifying hyperellipsoidal decision surfaces
The LVQ algorithm is a common method which allows a set of reference vectors for a distance classifier to adapt to a given training set. We have developed a similar learning algorithm, LVQ-MM, which manipulates hyperellipsoidal cluster boundaries as opposed to reference vectors. Regions of the input feature space are first enclosed by ellipsoidal decision boundaries, and then these boundaries are iteratively modified to reduce classification error. Results obtained by classifying the Iris data set are provided