To address the problem of ship target detection in optical remote sensing images under complex sea surface landform and cloud background conditions, an unsupervised ship target detection algorithm that combines the visual salient features of spatial and frequency domains is proposed. First, based on the RGB color space and the ITTI model of the images, image features are constructed using a combination of image brightness feature map, color feature map, and one-step brightness feature. Moreover, the regional difference in the image is calculated using the covariance matrix of the image region and the entire image. Further, the spatial-domain salient feature map is constructed using the generalized eigenvalue of the covariance matrix, and the frequency-domain salient feature map of the phase spectrum of quaternion Fourier transform (PQFT) model is added. Finally, the spatial- and frequency-domain salient features are combined using cellular automata. The experimental results show that the proposed algorithm is superior to other visual salient algorithms commonly used for ship target detection.