Texture-Sensitive Superpixeling and Adaptive Thresholding for Effective Segmentation of Sea Ice Floes in High-Resolution Optical Images

Abstract

Efficient and accurate segmentation of sea ice floes from high-resolution optical (HRO) remote sensing images is crucial for understanding of sea ice evolutions and climate changes, especially in coping with the large data volume. Existing methods suffer from noise interference and the mixture of water and ice caused high segmentation error and less robustness. In this article, we propose a novel sea ice floe segmentation algorithm from HRO images based on texture-sensitive superpixeling and two-stage thresholding. First, sparse components are extracted from the HRO images using the robust principal component analysis (RPCA), and noise is removed by the bilateral filter. The enhanced image is obtained by combining the low-rank matrix and the sparse components. Second, a texture-sensitive simple linear iterative clustering (SLIC) superpixel algorithm is introduced for presegmentation of the enhanced HRO image. Third, a learning-based adaptive thresholding in the two stages is employed to generate the refined segmentation from the derived superpixels blocks. The efficacy of the proposed method is validated on two HRO images using visual assessment, quantitative evaluation (with seven metrics), and histogram comparison. The superior performance of the proposed method has demonstrated its efficacy for sea ice floe segmentation.