Classification of natural scene multi spectral images using a new enhanced CRF

In this paper, a new enhanced CRF for discriminating between different materials in natural scenes using terrestrial multi spectral imaging is established. Most of the existing formulations of the CRF often suffer from over smoothing and loss of small detail, thereby deteriorating the information from the underlying unary classifier in areas with a high spatial frequency. This work specifically addresses this issue by incorporating a new pairwise potential that is better at taking local context into account. Certain materials are very unlikely to appear next to each other in the scene and such configurations are penalised by employing the confusion matrix of the unary classifier. Similarly, horizontal as well as vertical configurations, which may be more or less likely for certain combinations of materials, are regarded in this formulation. Furthermore, the proposed pairwise potential also considers the length of boundaries between regions to account for the segmentation granularity issues and also uses class probabilities of the neighbouring regions to make up for the uncertainty of the unary classifier results. Seven band terrestrial multi spectral imaging were used due to its potential in distinguishing between different materials and objects. The proposed approach was evaluated using cross-validation, resulting in an average accuracy of 88.9% which is about 17% more than the accuracy of a standard CRF, which demonstrates the superiority of our approach in preserving local details.