Enhanced spatially constrained remotely sensed imagery classification using a fuzzy local double neighborhood information c-means clustering algorithm

Abstract

This paper presents a fuzzy local double neighborhood information c-means (FLDNICM) clustering algorithm for remotely sensed imagery classification, which incorporates flexible and accurate local spatial and spectral information. First, a trade-off weighted fuzzy factor is established based on a pixel spatial attraction model that considers spatial distance and class membership differences between the central pixel and its neighbor simultaneously. This factor can adaptively and accurately estimate the spatial constraints from neighboring pixels. To further enhance robustness to noise and outliers, another fuzzy prior probability function is also defined, which integrates the mutual dependence information from a pixel and its neighbor in a fuzzy logical way for obtaining accurate spatial contextual information. The FLDNICM enhances the conventional fuzzy c-means algorithm by producing homogeneous segmentation while reducing the edge blurring artifacts. The new trade-off weighted fuzzy factor and prior probability function are both parameter free and fully adaptive to the image content. Experimental results demonstrate the superiority of FLDNICM over competing methodologies, considering a series of synthetic and real-world images classification applications.