Superpixel-Based Nearshore Ship Detection Method Enhanced by Fuzzy C-Means Clustering

Nearshore ship detection remains a challenging task due to the similarity in grayscale between harbor areas and ships. In this paper, we propose a new superpixel-based nearshore ship detection method, which effectively reduces land false alarms by the fuzzy C-means (FCM) algorithm. First, the method applies the simple linear iterative clustering (SLIC) algorithm to generate superpixel regions. Superpixels can preserve the boundary of the target and reduce the effects of speckle noise for target detection in synthetic aperture radar (SAR) images. Subsequently, we used the FCM algorithm to quantify the statistical differences between different super-pixels, and the clustering results can serve as an indicative measure of the probability that each superpixel belongs to the sea category. Finally, we incorporate this probability into our proposed saliency detection method, facilitating the efficient identification of the ship regions. The experimental results show that the method can robustly and efficiently detect nearshore ship targets.