Ships are vital marine targets. Real-time ship target detection based on aerial remote-sensing images is useful in civil engineering as well as national defense security. Based on the engineering application background, land-sea segmentation and ship detection was investigated in this study using aerial remote-sensing images. Gray-scale information was used to segment the remote-sensing images with adaptive threshold; the morphological operator and hole-filling technique were combined to achieve land-sea segmentation. Furthermore, ship geometry features were used to detect the straight-line segment, and K-means density clustering was applied to complete the ship target detection. The experimental results showed that for land-sea segmentation, the land detection rate was 95.8%, the land detection error rate was 5.7%, and the land detection accuracy rate was 94.4%. For ship target detection, the detection accuracy rate was 94.1%, and the detection false alarm rate was 3.9%. The proposed technique effectively segmented the ocean and land areas of the images, and the segmentation effect was ideal. The proposed technique had the following advantages: rapid ship target detection, high accuracy, low false alarm rate, simple calculation, and easy engineering applicability.