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

Foody, G.M. and Mathur, A. (2004) Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93 (1-2), 107-117. (doi:10.1016/j.rse.2004.06.017).

Abstract

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 acquired actually made a positive contribution to the analysis and allowed the crops to be classified to a high accuracy (92.5%). The majority of the training set, therefore, was unnecessary as it made no contribution to the analysis. Using ancillary information on soil type, however, it would be possible to constrain the training sample acquisition process. By limiting training sample acquisition only to regions with a specific soil type, it was possible to use a small training set to classify the data without loss of accuracy. Thus, a small number of intelligently selected training samples may be used to classify a data set as accurately as a larger training set derived in a conventional manner. The results illustrate the potential to direct training data acquisition strategies to target the most useful training samples to allow efficient and accurate image classification.

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