A structural similarity-based label-smoothing algorithm for the post-processing of land-cover classification

Abstract

ABSTRACTPost-processing is able to achieve a satisfactory classification performance with a low cost and simple assumption, making it widely used in the refinement of classification maps. In this study, a novel structural similarity-based label-smoothing algorithm is developed for the post-processing of land-cover classification. Inspired by the non-local (NL) means algorithm, the proposed algorithm assigns different voting weights to the neighbouring pixels for the identification of the central pixel. Here, the voting weight of a specific neighbouring pixel depends on its structural similarity to the central pixel. In this paper, two measurements are proposed to evaluate the similarity between pixels: (1) a consistency criterion; and (2) a histogram similarity criterion. The proposed algorithm was tested on three remote-sensing images. The experimental results confirm that the proposed algorithm reduces the classification noise and preserves the detail and structural information at the same time. Compared to the traditional post-processing approaches (e.g., majority voting), the proposed algorithm exhibits a more satisfactory performance.