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13 March 2003 Classification of hyperspectral imagery using limited training data samples
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The availability of only small samples of training data presents a problem when using statistical pattern recognition techniques. Recently new methods for pattern recognition, designed specifically for use with small training data samples, have begun to appear in the literature. These methods sample the training data many times to assemble a range of different classifiers. The classifiers produced may then be collected into an ensemble and, when presented with an unseen sample, use a voting scheme to determine class membership. A particular example of this ensemble classification technique, the random subspace method, is examined here and tested using both synthetic data having known properties, and with data from the AVIRIS hyperspectral-imaging sensor. The paper discusses the application of the method to problems that are not linearly separable; the selection of parameters for the method, and examines the performance envelope for different problems and parameterizations. Good results are produced for both datasets, even where the training samples are too small for conventional classification techniques to be used. Specifically, error rates of only twice those calculated for a large training sample may be achieved using training sets with as few as 20 examples per class, for a thirteen-class classification problem, using the 200-dimensional AVIRIS "Indian Pines" hyperspectral image.
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Chris J. Willis "Classification of hyperspectral imagery using limited training data samples", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003);

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