Land cover classification of high-resolution remote sensing images based on interval Type-2 fuzzy convolutional neural network

Abstract

This paper introduces an end-to-end approach for land cover classification utilizing high-resolution remote sensing images (HRRSI), leveraging an Interval Type-2 Fuzzy Convolutional Neural Network (IT2FCNN). This method employs fuzzy logic for nonlinear pixel mapping and adaptively identifies the bounds of Interval Type-2 Fuzzy Sets through fuzzy convolution operations. By incorporating multivariate Type-1 membership functions into conventional convolutional kernels, we have engineered fuzzy convolutional kernels. These kernels, along with fuzzy rule libraries, activate features derived from fuzzy convolutions, facilitating the iterative refinement of the model’s fuzzy sets. This hierarchical process culminates in the development of the IT2FCNN model. When applied to the Wuhan dense labeling dataset (WHDLD), our proposed method outperformed the latest Interval Type-2 Fuzzy Neural Network by 5.27% in accuracy across nine land cover categories. Furthermore, it demonstrated a 17.52% increase in accuracy on the UC Merced Land Use Dataset (UCM Dataset), particularly in dense residential areas, and an 18.3% improvement in sparse residential areas across eleven land cover categories. These results highlight the approach’s effectiveness in mitigating the impact of regional noise on land cover classification, showcasing its strong generalization capability and superior classification accuracy.