A comparison of stat-of-the-art superpixel methods for remote sensing data

Abstract

In computer vision, using superpixels or perceptually meaningful atomic regions to speed up later-stage processing are becoming increasingly popular in many applications. Superpixels are used as a pre-processing stage to organize an image into a low-level grouping process through oversegmentation, thus simplifying the computation in later stages. However, in remote sensing domain few studies use superpixels. Even so, there is no comparison between superpixel methods and their suitability for remote sensing images. In this study, we compare four state-of-the-art superpixel methods: Simple Linear Iterative Clustering (SLIC and SLICO), Superpixels Extracted via Energy-Driven Sampling (SEEDS) and Linear Spectral Clustering (LSC). We applied them to very high resolution remote sensing data of different characteristics (extent, spatial resolution and landscape complexity) in order to see how superpixels are affected by these factors. The four algorithms were compared regarding their computational time, ability to adhere to image boundaries and the accuracy of the resulted superpixels. Furthermore, we discuss the individual strengths and weaknesses of each algorithm and draw further applications of superpixels in OBIA.