## 1 Introduction
Nevertheless, these classical threshold segmentation methods are sensitive to noise and cannot detect small or low contrast ship targets due to the fact that their performance is easily affected by the clutter intensity information. To overcome the disturbance of noise and background clutter, the mean shift-based ship segmentation algorithms developed in [18; 19] have achieved considerable detection performance for infrared ship in sea clutter. Whereas, because the mean shift methods are based on region clustering and merging, they may obtain a wrong detection when the ship target has low contrast or point size. In addition, the active contour-based Chan-Vese models [20; 21] are also commonly used in the field of ship target detection because they can effectively segment the targets in homogenous background by extracting topology structure. Unfortunately, for complex background with heterogeneous sea clutter, the low-contrast ship target and sea clutter might be similar in topology structure, so serious false detection might happen. The human visual system-based methods are based on the local contrast measure and selective attention mechanism of the ship target region, and therefore, the feature saliency map calculation of infrared ship target is the foremost topic for HVS-based methods. [PERSON] et al. [22] adopted graph-based visual saliency (GBVS) method to compute a saliency map, and then extracted the ship target by using multilevel thresholding of the saliency map. The GBVS-based method may extract strongly salient clutter regions and fail to detect real ship target when the sea clutter is heavy. To conquer this problem, [PERSON] et al. [23; 24] proposed an effective infrared ship target detection method based on saliency map fusion by exploiting multi-features of ship target, including local contrast, edge information, and brighter intensity. Following, [PERSON] et al. [25] presented a new detection method for low-contrast infrared ship targets by analyzing the fuzzy inference system that integrates both local saliency information and global spatial feature. The two methods can acquire excellent performance for the detection of infrared ships in complex background clutter. However, these methods are based on the assumption that target regions are comparatively brighter than the dark sea surface, so they cannot detect the negative-source dark ship targets submerged in relatively bright backgrounds. In the machine learning-based methods, the infrared ship detection problem is considered as a two-class (ship target and background) recognition problem. In these methods, the infrared images are depicted by multiple feature vectors, and then the ship target class and background class can be distinguished by classifiers, such as feedforward neural network [26], extreme learning machine [27], artificial neural network [28], and convolutional neural network [29; 30]. These machine learning-based methods can easily detect diverse ship targets under intricate background clutter in some cases. However, regrettably, these methods must spend plenty of time training samples and selecting features. Moreover, in practical TIR maritime applications, these machine learning-based methods fail to generate enough training samples due to the complexity and variability of the sea clutter, leading to the deterioration of the ship target detection capability. Comparing the advantages and disadvantages of above-mentioned methods, although many studies have been focused on the detection of TIR ship targets against complex backgrounds in the past decades, it is still an open issue. Actually, in TIR remote sensing images, the natural scene background has strong local self-similarity, but the small ship target as a manmade object will destroy these characteristics of background, so compared with the background clutter even for heavy sea clutter, the ship target has solid intensity, contrast, contour, and shape features.