# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 2 Low-Resolution Sequence Target Detection Algorithm
### Preprocessing
The judgment basis for the target detected in the target window is:  
\[\delta_{e}=\frac{\mu_{T_{h}}}{\mu_{B}}>T_{Thr} \tag{2}\]  
where \(\mu_{T_{h}}\) is the mean value of the target window, \(\mu_{B}\) is the mean value of the background window, \(T_{Thr}\) is the comparison threshold, and \(\delta_{e}\) is the mean signal-to-noise ratio of the reconstruction result of the target region. The result of the binary image obtained by threshold segmentation is:  
\[Y_{b}(x,y)= \begin{cases}1,when\ \frac{\mu_{T_{h}}}{\mu_{B}}>T_{Thr}\\ 0,else\end{cases}  \tag{3}\]  
After adaptive threshold segmentation, the weak target is extracted according to the contract, which reduces the amount of image data to be processed later and eliminates background noise interference on the suspected weak target. It can be seen in Figure 3 that after adaptive segmentation, the dim target was significantly extracted, and the background noise was isolated. III. Connected Domain Labeling  
The connected domain set \(A_{i}=\left\{\overset{\rightarrow}{x}_{i},\overset{\rightarrow}{y}_{i}\right\} (i\in[0,P-1])\) is obtained by marking the connected domain of binary image \(Y_{b}\), \(P\) represents the number of regions, \(\overset{\rightarrow}{x}_{i}\) represents the set of \(x\) coordinates of the \(i\)-th region, and \(\overset{\rightarrow}{y}_{i}\) represents the set of \(y\) coordinates of the \(i\)-th region. In the annotation result, the small area is removed as a false alarm target, the large area is removed as cloud, land, and other targets, and the binary image \(Y_{b}^{\prime}\) is obtained. \[A_{i}^{\prime}= \begin{cases}A_{i},if\ S_{low}<Area_{i}<S_{high}\\ \phi,else.\end{cases}  \tag{4}\]  
where \(\phi\) represents null, \(S_{low}=5\) and \(S_{high}=100\) represent the lower and upper limits of the area, respectively, and \(Area_{i}\) represent the area of the \(A_{i}\) region. After the connected domain labeling and connected domain area elimination, the first-level suspected target set \(A_{i}^{\prime}(P^{\prime}\) regions) is obtained. In the preprocessing process, considering that the target width is about three pixels, the size of the square all \(`1\prime\) structure element \(\beta\) used in morphological reconstruction is \(3\times 3\). The number of reconstruction iterations \(R\) is related to the mean signal-to-noise ratio (SNR) \(\delta_{e}\) of the reconstruction results of the target area. In addition, it is related to illumination conditions, imaging side-swing angle, target sea state, and other conditions. In practical applications, considering the working mode, the same area is often observed in the same period. Therefore, \(\delta_{e}\) is observed after processing a large number of targets in the region; as shown in Figure 4, 80 groups of data were counted. When the number of iterations is 3, \(\delta_{e}\) reaches the optimum, and the optimal number of reconstruction iterations \(R_{optimal}=3\) is  
Figure 3: Comparison of remote sensing images before (**left**) and after (**right**) segmentation by adaptive threshold. obtained. The threshold \(T_{Thr}\) in adaptive threshold segmentation is mainly related to the mean/variance of the sea state in the imaging area and the gray level of the sea surface background. Considering that the gray level distribution of the sea surface tends to be unified after reconstruction at low resolution, 80 groups of data were statistically analyzed and \(T_{Thr}=1.2\) was obtained. The connected domain was marked, and the suspected target area was calculated. The area eliminated the larger and smaller targets, and the upper and lower limits of the area were \(S_{low}=10\) and \(S_{high}=50\), respectively.