A new co-learning method in spatial complex fuzzy inference systems for change detection from satellite images

The detection of spatial and temporal changes (or change detection) in remote sensing images is essential in any decision support system about natural phenomena such as extreme weather conditions, climate change, and floods. In this paper, a new method is proposed to determine the inference process parameters of boundary point, rule coefficient, defuzzification coefficient, and dependency coefficient and present a new FWADAM+ method to train that set of parameters simultaneously. The initial data are clustered simultaneously according to each data group. This result will be the basis for determining a suitable set of parameters by using the FWADAM+ concurrent training algorithm. Eventually, these results will be inherited in the following data groups to build other complex fuzzy rule systems in a shorter time while still ensuring the model’s efficiency. The weather imagery database of the United States Navy (US Navy) is used to evaluate and compare with some related methods using the root-mean-squared error (RMSE), R-squared (R2) measures, and the analysis of variance (ANOVA) model. The experimental results show that the proposed method is up to 30 better than the SeriesNet method, and the processing time is 10 less than that of the SeriesNet method. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.