Foreground ships can be quickly and effectively detected from sea background with the help of salient detection technology. As a result, saliency-analysis-based ship detection algorithms have received extensive research attention. However, obtaining accurate ship detection results influenced by irregular background noise, such as waves, clutter, and wakes, on the sea surface is challenging. A robust background-estimation-based salient ship detection algorithm has been proposed to solve the aforementioned problem. First, the input image is clustered into a set of superpixels, and the deep feature representation of each superpixel is extracted from a deep convolutional neural network. Then, a background noise estimation algorithm is proposed to effectively suppress the influence of background noise on ship detection and it is integrated into the solution framework of hierarchical cellular automata. Finally, the salient ship detection results can be obtained according to the difference in the feature description of various pixels in the stereo neighborhood space. Qualitative and quantitative experimental results demonstrate that the proposed algorithm could effectively enhance the salient ship detection effect under complex backgrounds.