Knowledge Based Stacking of Hyperspectral Data for Land Cover Classification

Hyperspectral data provide new capability for discriminating spectrally similar classes, but unfortunately such class signatures often overlap in multiple narrow bands. Thus, it is useful to incorporate reliable spatial information when possible. However, this can result in increased dimensionality of the feature vector, which is already large for hyperspectral data. Markov random field (MRF) approaches, such as iterated conditional modes (ICM), can provide evidence relative to the class of a neighbor through Gibbs' distribution, but suffer from computational requirements and curse of dimensionality issues when applied to hyperspectral data. In this paper, a new knowledge based stacking approach is presented to utilize spatial information within homogeneous regions and at class boundaries, while avoiding the curse of dimensionality. The approach learns the location of the class boundary and combines original bands with the extracted spectral information of a neighborhood to train a hierarchical support vector machine (HSVM) classifier. The new method is applied to hyperspectral data collected by the Hyperion sensor on the EO-1 satellite over the Okavango delta of Botswana. Classification accuracies are compared to those obtained by a pixel-wise HSVM classifier, majority filtering and ICM to demonstrate the advantage of the knowledge based stacking approach.