Spatial Logistic Regression for Support-Vector Classification of Hyperspectral Imagery

The traditional use of support-vector machines for hyperspectral imagery exploits spectral information alone; however, classifiers that incorporate spatial context have witnessed increasing interest due to their potential for significant improvement over spectral-only approaches. A new paradigm for spatial-spectral support-vector classification is introduced in which spatial context is included into the logistic regression commonly used with support-vector classifiers to provide a probabilistic output. In experimental results, the proposed approach is compared to methods representative of two prominent families of spatial-spectral support-vector classifiers-composite kernels and postprocessing regularization-and it is observed that the proposed approach provides superior classification accuracy.