Adaptive enhanced interval type-2 possibilistic fuzzy local information clustering with dual-distance for land cover classification

Abstract

Land cover classification of remote sensing image is faced with uncertainties such as “significant difference in category density”, “the same object with different spectra”, and “different objects with the same spectrum”. Existing possibilistic fuzzy clustering-related method still cannot fully meet the interpretation requirements of remote sensing data. To accurately classify remote sensing image with complex geographical distribution, this paper proposes a robust interval type-2 dual-distance driven possibilistic fuzzy clustering motivated by interval-valued number for land cover classification. Firstly, interval-valued data model is established by using the local mean and variance of single-valued data. Secondly, Hausdorff distance and MW distance for interval-valued numbers are introduced to realize the maximum separability of overlapping categories in interval-valued data and generate interval uncertainty sets. To further enhance its robustness, weighted local information factors are constructed by making full use of the generated fuzzy membership and possibilistic typicality, and a novel robust interval type-2 possibilistic fuzzy C-means clustering is proposed. Finally, the adaptive type reduction method is introduced, meanwhile the adaptive expansion of interval-valued data model is realized by using the adaptive contraction expansion control factor. After the iterative algorithm convergences, the accurate classification of covered objects in remote sensing images is realized. Experimental results indicate that the classification performance of the proposed algorithm is better than that of existing interval type-2 fuzzy clustering algorithm and its variants, and it is more suitable for the interpretation of remote sensing images in the actual environments.