Neuro-fuzzy logic for parts-based reasoning about complex scenes in remotely sensed data

In this article, we explore the role and usefulness of neuro-fuzzy logic in the context of automatically reasoning under uncertainty about complex scenes in remotely sensed data. Specifically, we consider a first order Takagi- Sugeno-Kang (TSK) adaptive neuro-fuzzy inference system (ANFIS). First, we explore the idea of embedding an experts knowledge into ANFIS. Second, we explore the augmentation of this knowledge via optimization relative to training data. The aim is to explore the possibility of transferring then improving domain performance on tedious but important and challenging tasks. This route was selected, versus the popular modern thinking of learning a neural solution from scratch in an attempt to maintain interpretability and explainability of the resultant solution. An additional objective is to observe if the machine learns anything that can be returned to the human to improve their individual performance. To this end, we explore the task of detecting construction sites, an abstract concept that has a large amount of inner class variation. Our experiments show the usefulness of the proposed methodology and it sheds light onto future directions for neuro-fuzzy computing, both with respect to performance, but also with respect to glass box solutions.