## 3 Ship Target Detection based on Morphological Reconstruction and Multi-feature Analysis  
### Proposed Bright Ship Target Detection in TIR Images  
Most studies [11; 23; 25] indicated that the background is the comparatively dark sea surface and the ship targets might be relatively local brighter regions. According to these above-mentioned methods and theories, the accurate bright ship target detection algorithm for TIR image is established. Firstly, we perform the opening operation on the original TIR image and reconstruct the original image from the result image after the opening operation by OGMR. Therefore, the background of the bright ship target image is estimated by OGMR, and then the IFSM is computed and normalized. Meanwhile, we perform the closing operation on the original image, and reconstruct the original image from the image after closing operation by CGMR. Accordingly, the pre-processing result of bright ship image is obtained by CGMR, and then the BCSM is calculated and normalized. Next, the final saliency map is generated by fusing normalized IFSM and normalized BCSM in a pixel-wise multiplication fusion manner, and the binary segmentation result of the final saliency map is acquired by an adaptive threshold. The whole of the proposed saliency map fusion algorithm for TIR bright ship image based on GMR is summarized in Algorithm 1. It is noteworthy that the Algorithm 1 not only can be used to highlight the potential bright ship targets buried in extremely serious rolling sun-glint clutter, but also can be used to directly detect the ship targets submerged in common sea clutter. ```
Input: TIR ship target image \(f\), structuring element \(B\). Output: Binary final saliency map for bright ship target image \(BSM\). Step 1: Perform the opening operation with structuring element \(B\) on \(f\) to acquire \(g\) using (4). Step 2: Compute the elementary geodesic dilation \(g^{(i)}\) according to (9). Step 3: Reconstruct \(f\) from \(g\) based on OGMR \(OR_{f}[g]\) derived from (8). Step 4: Obtain the intensity foreground saliency map \(IFSM_{b}\) of \(f\) according to (10) Step 5: Normalize the intensity foreground saliency map \(\mathbf{N}[IFSM_{b}]\) using (16). Step 6: Perform the closing operation on \(f\) to acquire \(h\) using (5). Step 7: Compute the elementary geodesic erosion \(ge^{(i)}\) according to (12). Step 8: Acquire the pre-processing result by reconstructing \(f\) from \(h\) based on CGMR \(OR_{f}[h]\) derived from (11). Step 9: Calculate the brightness contrast saliency map \(BCSM_{b}\) on pre-processed image \(OR_{f}[h]\) using (14). Step 10: Normalize the brightness contrast saliency map \(\mathbf{N}[BCSM_{b}]\) according to (16). Step 11: Generate the final saliency map \(SM_{b}\) based on pixel-wise multiplication fusion manner according to (17). Step 12: Calculate the adaptive threshold \(\tau\) according to (19). Step 13: Acquire the binary segmentation result \(BSM\) of the final saliency map \(SM_{b}\) according to (20)
```  
**Algorithm 1** Proposed saliency map fusion algorithm for TIR bright ship image  
In order to further identify the true ship targets from residual clutter, a reliable two-step ship verification method for TIR ship images is presented. Firstly, we compute the normalized large eigenvalue map of structure tensor of CGMR pre-processed image and calculate the STAEM values of each labeled connected region in the binary segmentation result of the final saliency map. Then, the map of candidate ship targets is obtained by eliminating the connected regions where the STAEM value is smaller than an experimentally distinctive threshold \(Thr^{*}\). Finally, the output detected infrared \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & **Min** & **Average** & **Max** \\ \hline \(Ratio_{muni}\) & 1.1002 & 3.5105 & 8.7857 \\ _Compactness_ & 11.2813 & 48.1344 & 118.6254 \\ _Rectangularity_ & 0.3826 & 0.6025 & 0.8769 \\ \hline \hline \end{tabular}
\end{table}