Revisiting SLIC: Fast Superpixel Segmentation of Marine SAR Images Using Density Features

Abstract

The simple linear iterative clustering (SLIC) has been shown as an efficient and widely used superpixel-based algorithm for segmenting marine synthetic aperture radar (SAR) images. However, SLIC does not consider the fact that the density of ship target pixels is significantly lower than that of sea clutter pixels, leading to a waste of computational cost and memory resources on lots of pure clutter areas and to the degradation of the compactness of superpixels. To address the aforementioned issues, we develop a new density-based SLIC (DSLIC) method for the superpixel-based segmentation of marine SAR images. In the initialization stage of our DSLIC, all the subimages in a large marine SAR image are rapidly prescreened via a new density-driven classifier, where most of the subimages only occupied by clutter pixels with comparatively high density are discarded and do not need to be segmented in the subsequent local clustering stage. The retained subimages contain both the clutter and potential target areas. This prescreening operation results in higher computation efficiency and memory savings. In the local clustering stage of DSLIC, besides the intensity proximity and the spatiality proximity (used in SLIC), the sparsity proximity (measured by density distances) is considered to reduce the coexistence of sparse target pixels with low density and nonsparse clutter pixels with high density within superpixels. Our theoretical and experimental results show that the proposed DSLIC method is faster and requires less memory than SLIC and other state-of-the-art superpixel-based segmentation methods for marine SAR images with similar or better segmentation accuracy.