On the basis of studying the theories of classical ITTI visual attention models, the defects of traditional visual models applied to sea-surface SAR images are summarized according to the characteristics of the background and the target of sea-surface SAR images. A visual attention model design algorithm for sea-surface SAR images is proposed. Firstly, the model uses the basic framework of the classical ITTI model, selects and extracts the texture and shape features that can describe the SAR image well. Then the corresponding saliency map of features is obtained. Secondly, the new integration mechanism of the saliency map of features is adopted to replace the linear-adding mechanism of the classical model for fusing the saliency maps and obtaining the overall saliency map. Finally, the gray features of the attention focus of all the saliency maps are integrated to select the optimal significance characterization. By using the multi-scale competitive strategy, the filtering and threshold segmentation are completed to realize the accurate screening of significant areas. Therefore, the detection of the significant areas of SAR images is completed. Experiments were carried out by using Terra-SAR-X and other satellite data, and their results verified the good significance-detection effects of the model. The model can better meet the demands of the detection of high-resolution image targets. By carrying out further comparative analysis with the classical visual model, it is discovered that the proposed algorithm can not only reduce the impact of the false alarm caused by speckle noise and uneven sea-clutter background on the detection result, but also greatly improve the detection speed by 25% to 45%.