A low-cost classifier for multitemporal applications†

Abstract
Remote sensing image classification with the maximum-likelihood decision rule leads to a computational cost that depends quadratically on the number of bands in the data. Moreover, the data has to be modelled beforehand by sets of multivariate normal distributions if acceptable classification accuracies are to be obtained. A new algorithm is presented with a cost linearly proportional to the number of bands. Being based upon a combination of linear classifiers it is not dependent upon a priori parametric modelling of the data. Instead it partitions the measurement space in a piecewise-linear fashion leading to high accuracies at low cost, particularly for multitemporal data.

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