FuzzyVGG: Improving Uncertainty and Interpretability in Satellite Image Classification through Fuzzy Logic Integration: Improving Uncertainty and Interpretability inSatellite Image Classification through Fuzzy LogicIntegration - Slimani, Nawel; Hcini, Ghazala; Jdey, Imen; Kherallah, Monji (Revista de Informática Teórica e Aplicada, 2026)
Convolutional neural networks (CNNs) have demonstrated impressive performance in image recognition tests due to their deep hierarchical feature extraction capabilities. But when it comes to satellite imagery, which frequently has inherent uncertainties like shifting lighting, unclear object boundaries, and overlapping classes, these conventional models provide serious difficulties. Standard CNN architectures may perform less well as a result of these ambiguities. To address these limitations, this study introduces a novel hybrid approach called Fuzzy VGG, which integrates fuzzy logic into the VGG16 and VGG19 architectures. The primary objective is to enhance model interpretability and robustness in handling the uncertainties present in satellite image data. Our method replaces the conventional dense-softmax output layer with a custom fuzzy classification layer that represents soft class membership using cosine similarity. Our experiments on the UC Merced Land Use dataset show that the integration of fuzzy logic leads to significantly improved performance. The Fuzzy VGG16 model achieved an accuracy of 90%, while Fuzzy VGG19 reached 97% accuracy. This represents a consistent performance gain of 15% over the standard VGG16 (75% accuracy) and VGG19 (80% accuracy) models. These results confirm the effectiveness of our hybrid system in creating a more robust and reliable decision-making mechanism for complex image classification tasks. On a medical dataset, the fuzzy VGG model achieved an overall accuracy of 96%, with macro and weighted averages for precision, recall, and F1-score all equal to 96%.