A novel spatial complex fuzzy inference system for detection of changes in remote sensing images

To enhance the efficacy of change detection in remote sensing images, we propose a novel Spatial Complex Fuzzy Inference System (Spatial CFIS). This system incorporates fuzzy clustering to generate complex fuzzy rules and employs a triangular spatial complex fuzzy rule base to predict changes in subsequent images compared to their original versions. The weight set of the rule base is optimized using the ADAM algorithm to boost the overall performance of Spatial CFIS. Our proposed model is evaluated using datasets from the weather image data warehouse of the USA Navy and the PRISMA mission funded by the Italian Space Agency (ASI). We compare the performance of Spatial CFIS against other relevant algorithms, including PFC-PFR, SeriesNet, and Deep Slow Feature Analysis (DSFA). The evaluation metrics include RMSE (Root Mean Squared Error), R2 (R Squared), and Analysis of Variance (ANOVA). The experimental results demonstrate that Spatial CFIS outperforms other models by up to 40% in terms of accuracy. In summary, this paper presents an innovative approach to handling remote sensing images by applying a spatial-oriented fuzzy inference system, offering improved accuracy in change detection.