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Very high spatial resolution satellite images, acquired by third-generation commercial remote sensing (RS) satellites (like Ikonos and QuickBird), are characterized by a tremendous spatial complexity, i.e. surface objects are described by a combination of spectral, textural and shape information. Potentially capable of dealing with the spatial complexity of such images, context-sensitive data mapping systems, e.g. employing filter sets designed for texture feature analysis/synthesis, have been extensively studied in pattern recognition literature in recent years. In this work, four implementations of a two-stage classification scheme for the analysis of high spatial resolution images are compared. Competing first stage (feature extraction) implementations of increasing complexity are: 1) a standard multi-scale dyadic Gaussian pyramid image decomposition, and 2) an original almost complete (near-orthogonal) basis for the Gabor wavelet transform of an input image at selected spatial frequencies (i.e. band-pass filter central frequency and filter orientation pairs). The second stage of the classification scheme consists of: a) an ensemble of pixel-based two-class support vector machines (SVMs) applied to the multi-class classification problem according to the one-against-one strategy, exploiting the well-known SVM's capability of dealing with high dimensional mapping problems; and b) a traditional two-phase supervised learning pixel-based Radial Basis Function (RBF) network. In a badly-posed Ikonos image classification experiment, SVM combined with the two filter sets provide an interesting compromise between ease of use (i.e. easy free parameter selection), classification accuracy, robustness to changes in surface properties, capability of detecting genuine, but small, image details as well as linear structures. Qualitatively and quantitatively, the multi-scale multi-orientation almost complete Gabor wavelet transform appears superior to the dyadic multi-scale Gaussian pyramid image decomposition, in line with theoretical expectations. Further experiments confirm that the novel implementation of a sample-based SVM classifier combined with the multi-scale Gabor wavelet transform provides a viable strategy for dealing with the spatial complexity of high spatial resolution RS image mapping problems.
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