## 1 Introduction  
Ships are important targets of real-time monitoring and wartime attacks at sea, and their accurate and fast detection can play a key role in the analysis of enemy situations, precision guidance, and military mapping. Ships also play an irreplaceable role in rescue, the safety management of fishing vessels, and so on. However, automatic detection is prone to false alarm and missed detection due to complex interference such as shooting weather, sea surface clutter, cloud, fog occlusion, and uneven illumination. How ships can be quickly and reliably detected in and extracted from panchromatic remote sensing images (with one channel) has become a critical issue [1; 2; 3]. At present, the ship detection method mainly includes three main stages [4; 5; 6; 7]: the separation of sea and land, the region of interest (ROI) location, and target feature description and false alarm elimination, and the last two steps have received an increasing amount of attention. In the stage of locating ROIs (our interest areas of the suspicious ship in the image), gray statistical features, edge detection, and visual saliency are the most popular tools. The first two kinds of algorithm are simple to implement, but when sea conditions become complicated because the wind and waves are strong, the contrast of targets and backgrounds is low, and the position effect is poor. Even a ship target could be submerged into the background under these circumstances. In recent years, methods based on visual saliency models have been successfully applied in target localization, and these methods simulate the human visual system and quickly focus on the ROIs in the image. Visual saliency models can be mainly divided into two types: top-down and bottom-up models. The top-down models, which use cognitive factors such as pre-knowledge, context information, expectations, and motivations to perform a visual search, are related to specific tasks and goals. This model belongs to an advanced cognitive process to consciously calculate the characteristics according to the task, and the existing top-down models usually bring in high computational costs and do not have a generic model. Therefore, the top-down models are more complicated and are rarely used in engineering projects. Most of the saliency detection models, including the spatial domain and transform domain, are bottom-up. In [8], ITTI constructed a saliency map based on intensity, color, and orientation features. This method has a good implementation; however, the model performed in the spatial domain is relatively complicated in structure and intensive in computation. [PERSON] proposed a phase spectrum quaternary Fourier transform (PQFT) method for color images, and the edge detection performance was satisfactory, but the continuity of the segmentation region was poor with respect to panchromatic images [9]. [PERSON] proposed the spectral residual method (SR), which is a significant detection model based on phase spectrum analysis in the frequency domain [10]. [PERSON] proposed the hyper-complex frequency domain transform model (HFT) with higher significant detection accuracy in simple scenes [11]. [PERSON] proposed a novel visual saliency detection method based on differences in statistical characteristics to locate ROIs [12]. [PERSON] presented a global saliency model based on high-frequency coefficients of multi-scale and multi-direction wavelet decomposition for ship detection [13]. Generally speaking, the processing results of the aforesaid algorithms were unsatisfactory with respect to panchromatic images. Specifically, these algorithms were not only sensitive to the boundaries of the target areas, but the detected target area was also incomplete in complex backgrounds. With respect to the feature description and false alarm elimination phase [14; 15; 16], there are two representative categories. One is the method based on the statistical analysis of targets and false alarm characteristics.