Modeling and Classifying Hyperspectral Imagery by CRFs With Sparse Higher Order Potentials

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. The recently defined conditional random field (CRF) can effectively model and use the dependencies for classification of hyperspectral images in a unified probabilistic framework. However, in order to be computationally tractable, the usual CRFs are limited to incorporate only pairwise potentials. Thus, the usual CRFs can capture only pairwise interactions and neglect higher order dependencies, which are potentially useful high-level properties particularly for the classification of hyperspectral image consisting of complex components. This paper overcomes this limitation by developing hyperspectral image classification algorithm based on a CRF with sparse higher order potentials, which are specially designed to incorporate complex characteristics of hyperspectral images. To efficiently implement the CRF model at training step, this paper develops an efficient local method under the piecewise training framework, while at inference step, this proposes a simple strategy to combine the piecewisely trained model to overcome the possible over-counting problems. Moreover, the combined model with the specially defined potentials can be efficiently inferred by graph cut method. Experiments on the real-world data attest to the accuracy, effectiveness, and efficiency of the proposed model on modeling and classifying hyperspectral images.