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Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification by Giles M. Foody; Ajay Mathur is a Environmental Science article available to read on EtoBox.

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Conventional approaches to training a supervised image classification aim to fully describe all of the classes spectrally. To achieve a complete description of each class in feature space, a large training set is typically required. It is not, however, always necessary to have training statistics that provide a complete and representative description of the classes, especially if using nonparametric classifiers. For classification by a support vector machine, only the training samples that are support vectors, which lie on part of the edge of the class distribution in feature space, are required; all other training samples provide no contribution to the classification analysis. If regions likely to furnish support vectors can be identified in advance of the classification, it may be possible to intelligently select useful training samples. The ability to target useful training samples may allow accurate classification from small training sets. This potential for intelligent training sample collection was explored for the classification of agricultural crops from multispectral satellite sensor data. With a conventional approach to training, only a quarter of the training samples acq

Who reads Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification?

Author

Giles M. Foody; Ajay Mathur

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0034-4257)

Published

2004

Language

EN

Field

Environmental Science (Physical Sciences)