To solve the problems of low ship-target detection rate and low multi-spectral near-infrared (NIR) band utilization rate of optical remote sensors in complex background, a novel algorithm for saliency detection of ship targets based on four-band multi-spectral remote sensing images is proposed. The proposed algorithm employs the features of visual images in four-band remote sensing data that have rich color information as well as NIR images that have good ability to describe details. First, the three bands of blue, green, and red images are transformed into the CIE-Lab color space. Then, the NIR image is decomposed via the non-subsampled contourlet transform. The obtained high-frequency components are nonlinearly enhanced to suppress noise and enhance details, and the low-frequency components are enhanced via unsharp masking to improve the uniformity of image brightness. The high-frequency components and low-frequency components are combined with the brightness images in Lab space to obtain a new Lab image. Finally, the maximum symmetric surround model is applied to the new Lab image to obtain a saliency image of the ship target. The experimental results show that the proposed algorithm can fully suppress the complex background information of clutter interferences, such as cloud waves and sea wakes, and it also can highlight ship targets in low contrast backgrounds. The proposed algorithm has good precision and recall.