## 1 Introduction
To further overcome the disturbance of heavy sea clutter on the detection of ship targets with low-contrast, multiple targets, unknown brightness, and different sizes, we propose an effective and robust small ship detection scheme, based on the morphological reconstruction and multi-feature analysis of TIR imaging characteristics between ship targets and background clutter. Firstly, a pre-processing procedure based on closing (opening)-based gray-level morphological reconstruction (GMR) is introduced to remove noise and smooth intricate background clutter while preserving the ship target signals including intensity, shape, and contour information. Secondly, according to the intensity and local region contrast features of TIR ship targets, the intensity foreground saliency map (IFSM) and brightness contrast saliency map (BCSM) are computed and fused to well enhance potential ship targets, and an adaptive threshold is applied to segment candidate ship targets. Then, motivated by the contour characteristic of target region in GMR pre-processed image, a novel contour descriptor of TIR ship target named as average eigenvalue measure of structure tensor (STAEM) is proposed to characterize candidate ship targets and eliminate residual clutter simultaneously. After that, based on the statistics and observation of shape parameters of TIR ship targets selected from a comprehensive ship database namely visible and infrared ships (VAIS) [31], shape knowledge is obtained and utilized to further distinguish true ship targets from non-ship targets. Finally, the dual approach is adopted to detect both bright and dark ship targets in TIR image simultaneously. Extensive experiments show that the proposed ship target detection scheme outperforms the compared state-of-the-art algorithms under diverse backgrounds, and is suitable for ship targets with unknown brightness, variable sizes, and quantities. Figure 1 gives the flow chart of the proposed TIR small ship target detection method. The red boxes have enlarged true ship targets, and the purple boxes have marked several highest STAEM values of false targets. The STAEM value of ship target is 0.3281, and the four largest STAEM values of false targets are 0.1431, 0.1149, 0.0960, and 0.0792, respectively. \(\otimes\) denotes pixel-wise multiplication manner, and \(\oplus\) denotes pixel-wise addition manner. Figure 1a is the input TIR ship image, and the final automatically detected ship target image is shown in Figure 1g. During this process, a dual approach for both bright and dark ship target detection is introduced. Figure 1b1-b4 shows the  
GMR-based pre-processed images, and Figure 1c1-c4 gives the saliency detection results of IFSM and BCSM. Figure 1(d1,d2) shows the final fused saliency maps of bright and dark ship target, respectively. Figure 1(e1,e2) is the step of extracting candidate ship targets and eliminating residual clutter by STAEM. Figure 1(f1,f2) shows the detected bright and dark ship target maps after two-step ship verification method, respectively. There are four contributions in this paper: (1) Traditional infrared ship target detection methods suffer the disturbance of heavy sea clutter. In this paper, the GMR-based pre-processing procedure is introduced to efficiently remove noise and smooth intricate background clutter. Moreover, to deeply explore the intensity and local region contrast features of TIR ship targets after GMR-based pretreatment, the IFSM and BCSM are computed and fused to highlight potential ship targets and suppress sea clutter. As far as we know, it is the first time that gray-level morphological reconstruction is used for suppressing heavy sea clutter and perceiving the saliency map detection for potential ship targets. (2) The STAEM is presented as a valid contour descriptor to further depict the candidate ship targets and eliminate residual clutter simultaneously. The proposed STAEM is a novel measure to describe the contour information of a connected region.