Classification of High Spatial Resolution Images by means of a Gabor Wavelet Decomposition and a Support Vector Machine

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 freq...