Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping

This paper explores the effects of class separability in Markov-random-field-based superresolution mapping (SRM). We propose to account for class separability by means of controlling the balance tuned by a smoothness parameter between the prior and the likelihood terms in the posterior energy function. A generally applicable procedure estimates the optimal smoothness parameter, based on local energy balance analysis. The study shows how the optimal value of the smoothness parameter depends quantitatively and monotonically upon the class separability and the scale factor. Effects are studied on an image synthesized from an agricultural scene with field boundary subpixels. We varied systematically the class separability, the scale factor, and the smoothness parameter values. The accuracy of the resulting land-cover-map image is assessed by means of the kappa statistic at the fine-resolution scale and the class area proportion at the coarse-resolution scale. Performance is compared with a hard and a soft classification of the coarse-resolution image. We demonstrate that an optimal value of the smoothness parameter exists for each combination of scale factor and class separability. This allows us to reach a high classification accuracy (kappa = 0.85) even for poorly separable classes, i.e., with a transformed divergence equal to 0.5 and a scale factor equal to 10. The developed procedure agrees with the empirical data for the optimal smoothness parameter. The study shows that SRM is now applicable to a larger set of images with class separability ranging from poor to excellent.