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

KERNEL: A Matlab Toolbox for Knowledge Extraction and Refinement by NEural Learning

n/

Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks

n/

Exploiting Particle Swarm Optimization to Attune Strong Fuzzy Partitions Based on Cuts

Cut-based strong fuzzy partitions (SFP) are characterized by cuts, i.e. points in the universe of discourse where the non-zero membership degrees of the fuzzy sets in the partition is 0.5. Cuts are useful to identify the most representative regions for the fuzzy sets involved in a SFP but pose loose constraints on the slopes of trapezoidal fuzzy sets. We address the problem of optimizing such slopes in order to maximize the performance of fuzzy rule-based systems while keeping cuts constant. This way, model performance is improved and interpretability is preserved. We use Particle Swarm Optimization to perform optimization and we analyze two different approaches for generating solution spaces. We tested the proposed approach on a number of fuzzy rule-based classifiers designed by DC* on synthetic data. For all the considered models, performance is never degraded but improved in many cases, without violating any interpretability constraint