Semantic Annotation of Land Cover Remote Sensing Images Using Fuzzy CNN

This paper presents a novel fuzzy logic based Convolution Neural Network intelligent classifier for accurate image classification. The proposed approach employs a semantic class label model that classifies the input land cover images into a set of semantic categories and classes depending on the content. The intelligent feature selection algorithm selects the prominent attributes from the given data set using weighted attribute functions and uses fuzzy logic to build the rules based on the membership values. To annotate remote sensing images, the CNN method effectively creates semantics and categorises images. The decision manager then integrates the fuzzy logic rules with the CNN algorithm to achieve accurate classification. The proposed approach achieves a classification accuracy of 90.46% when used with various training and test images, and the three class labels for vegetation (84%), buildings (90%), and roads (90%) provide a higher classification accuracy than other existing algorithms. On the basis of true positive rate, false positive rate, and accuracy of picture classification, the suggested approach outperforms the existing methods.