Interpretable Rule-Based Fuzzy ELM and Domain Adaptation for Remote Sensing Image Classification

The size of the remote sensing (RS) data is increasing exponentially with modern technological evolution. Efficient mapping with these data using a supervised model demands ample labeled samples, and obtaining them is expensive and time-consuming. For that reason, domain adaptation (DA) methodologies that work with limited available knowledge in terms of labeled samples or trained models are highly successful. DA uses the model trained for a domain called “source” and aims to classify the data of a related domain called “target.” However, an insufficient amount of labeled samples in the target domain data set is still a bottleneck for DA models used for RS images. To address this, we have proposed an efficient DA classification model using an interpretable rule-based fuzzy extreme learning machine (IRF-ELM). These rules are derived using the maximum fuzzy membership value of features characterized by class-belonging fuzzification and two rule extraction matrices. A partially connected ELM architecture is then designed with these rules. The fuzzification process typically aligns the target domain's feature space to the source domain in the best possible ways to transfer knowledge between them. The proposed model's superiority to similar other methods is verified using different RS data sets in terms of various performance measurement indexes.