Automatic ship detection from remote sensing images is very important as a variant of applications such as harbor management, cargo shipping, marine rescue and naval warfare will call for the aids of the analysis of these images. This paper focuses on the processing of space-born optical images (SDSOI). With the continuous development of photography technology, high-resolution remote sensing images are produced with extremely high speed, but still lack of an effective and swift method to automatically process them and get an applicable result. The whole work flow is based on three modules. First, separating land and sea with threshold segmentation, texture segmentation and region-growth and hollow-filling algorithm, and extract the sea region as ROI. Second, apply contrast box algorithm to the ROI to get the candidates of targets. Thirdly, use shape analysis to delete some simple false candidates, and use the saliency map algorithm to eliminate possible influence of clouds. Experimental results of a series of optical remote sensing images captured by satellites indicate that our approach is effective and swift in dealing with high resolution SDSOI, obtains a satisfactory ship detection miss rate and alarm rate.