Incorporating Spatial Contiguity into the Design of a Support Vector Machine Classifier

Abstract

We describe a modification of the standard support vector machine (SVM) classifier that exploits the tendency for spatially contiguous pixels to be similarly classified. A quadratic term characterizing the spatial correlations in a multispectral image is added into the standard SVM optimization criterion. The mathematical structure of the SVM programming problem is retained, and the solution can be expressed in terms of the ordinary SVM solution with a modified dot product. The spatial correlations are characterized by a "contiguity matrix" psi whose computation does not require labeled data; thus, the method provides a way to use a mix of labeled and unlabeled data. We present numerical comparisons of classification performance for this contiguity-enhanced SVM against a standard SVM for two multispectral data sets.