In target detection on aerospace optical remote sensing, due to the interference of uncertain conditions on sea surface such as atmosphere, solar radiation, cloud and mist, islands and others, traditional ship detection methods always have some defects such as low detection efficiency, poor reliability. Therefore, the author proposed an unsupervised ship automatic detection method. In this method, visual saliency was combined with multi-saliency detection model for fast searching of sea-surface targets; after saliency map was generated, a rough segmentation was conducted on it, then extracted target slice was marked and fine segmentation was implemented, subsequently, improved Hough transformation was used to rotate principal axis of target for ensuring the symmetry of targets to Y axis; the characteristics of gradient direction was applied to recognize phony targets such as thick clouds layer, islands and others that may be detected, the gradient and amplitude statistical value of those targets in 8 intervals on all directions were judged to identify target ships and warships and eliminate phony targets. The experimental result indicates that the detection method of ships and warships can be used to successfully extract target ships and warships which are in different size and random distributed on sea surface for obtaining accurate quantity and location information about them. In test on a large number of authentic optical remote sensing pictures, the detection accuracy rate of proposed method is higher than 93%, while the false alarm rate is lower than 4% through target identification and treatment and phony target elimination, which has strong robustness.