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Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of the classifier are the most important. This paper evaluates the effects of these factors on classification accuracy using a test area in La Mancha, Spain. High spectral and spatial resolution DAIS data were used to compare the performance of four classification procedures (maximum likelihood, neural network, support vector machines and decision tree). There was no evidence to support the view that classification accuracy inevitably declines as the data dimensionality increases. The support vector machine classifier performed well with all test data sets. The use of the orthogonal MNF transform resulted in a decline in classification accuracy. However, the decision‐tree approach to feature selection worked well. Small increases in classifier accuracy may be obtained using more sophisticated techniques, but it is suggested here that greater attention should be given to the collection of training and test data that represent the range of land surface variability at the spatial scale of the image. [ABSTRACT FROM AUTHOR]

The article highlights the Second International Symposium on Recent Advances in Quantitative Remote Sensing held in Torret, Spain from September 25 to 29, 2006. The event was sponsored and organized by the Global Change Unit of the University of Valencia, Spain. The symposium addressed the scientific advances in quantitative remote sensing and their connection with real applications. The event's main goal was to assess the most recent developments in both theory and applications in the field of remote sensing.

Highlights the First International Symposium on Recent Advances in Quantitative Remote Sensing in Torrent, Spain. Sponsorship of the symposium.