Ship detection in optical remote sensing images based on saliency and rotation-invariant feature

Abstract

Ship detection is important to guarantee maritime safety at sea. In optical remote sensing images, the detection efficiency and accuracy are limited due to the complex ocean background and variant ship directions. Therefore, we propose a novel ship detection method, which consists of two main stages: candidate area location and target discrimination. In the first stage, we use the spectral residual method to detect the saliency map of the original image, get the saliency sub-map containing the ship target, and then use the threshold segmentation method to obtain the ship candidate region. In the second stage, we obtain the radial gradient histogram of the ship candidate region and transform it into a radial gradient feature, which is rotation-invariant. Afterward, radial gradient features and LBP features are fused, and SVM is used for ship detection. Data experimental results show that the method has the characteristics of low complexity and high detection accuracy.