Detection of built-up area expansion in ASTER and SAR images using conditional random fields

The heterogenous land-cover structure in built-up areas challenges existing classification methods. This study developed a method for detecting such areas from SAR and ASTER images using conditional random fields (CRFs). A feature selection approach and a novel data dependent term were designed and used to classify image blocks. A new approach of discriminating classes using variogram features was introduced. Mean, standard deviation and variogram slope features were used to characterize training areas including spatial dependencies of classes. The association potential was designed using support vector machines (SVMs) and the inverse of transformed Euclidean distance used as a data dependent term of the interaction potential. The latter maintained a stable accuracy when subjected to a variation of a smoothness parameter while preserving class boundaries and aggregating similar labels during classification. In this way, a discontinuity adaptive model that moderated smoothing given data evidence was obtained. The accuracy of detecting built-up areas using CRF exceeded that of Markov Random Fields (MRF), SVM and maximum likelihood classification (MLC) by 1.13%, 2.22% and 8.23% respectively. It also had the lowest fraction of false positives. Application of the method showed that built-up areas increased by 98.9 ha while 26.7 ha was converted from built-up to non-built-up areas. We conclude that the new procedure can be used to detect and monitor built-up area expansion; in this way it provides timely spatial information to urban planners and other relevant professionals.