Current learning approaches to computer vision have mainly focussed on low-level image processing and object recognition, while tending to ignore higher level processing for understanding. We propose an approach to scene analysis that facilitates the transition from recognition to understanding. It begins by segmenting the image into regions using standard approaches, which are then classified using a discovered fuzzy Cartesian granule feature classifier. Understanding is made possible through the transparent and succinct nature of the discovered models. The recognition of roads in images is taken as an illustrative problem. The discovered fuzzy models while providing high levels of accuracy (97%), also provide understanding of the problem domain through the transparency of the learnt models. The learning step in the proposed approach is compared with other techniques such as decision trees, naive Bayes and neural networks using a variety of performance criteria such as accuracy, understandability, and efficiency.