Segmentation of remotely sensed images with a neuro-fuzzy inference system

The semantic segmentation of remotely sensed images is a difficult task because the images do not represent well-defined objects. To tackle this task, fuzzy logic represents a valid alternative to convolutional neural networks—especially in the presence of very limited data—, as it allows to classify these objects with a degree of uncertainty. Unfortunately, the fuzzy rules for doing this have to be defined by hand. To overcome this limitation, in this work we propose to use an adaptive neuro-fuzzy inference system (ANFIS), which automatically infers the fuzzy rules that classify the pixels of the remotely sensed images, thus realizing their semantic segmentation. The resulting fuzzy model guarantees a good level of accuracy in the classification of pixels despite the few input features and the limited number of images used for training. Moreover, unlike the classic deep learning approaches, it is also explanatory, since the classification rules produced are similar to the way of thinking of human beings.