# Multiscale Feature-Based Infrared Ship Detection
## 3 Results  
Here, different sea surface scene images were selected for testing, in which scenes 1-2 were collected using medium-wave thermal imagers without coolers, and scenes 3-6 were collected using medium-wave refrigerated thermal imagers, with the resolutions set as 640 pixels \(\times\) 512 pixels. All the experiments were carried out in MATLAB R2018b, on a computer with Intel(R) Core(TM) i5 CPU and 8 GB memory. To provide a comprehensive assessment, we compared our method with five other algorithms: top-hat, K-means clustering, fuzzy C-means clustering (FCM), mean shift, and grab-cut (GC). The results of all the tested algorithms, including our method and the five comparison algorithms, are presented in Figure 11. These results allow for a visual comparison and analysis of the segmentation outcomes obtained by each algorithm on the tested sea surface scene images. Figure 11a shows the original infrared image in different scenarios. Interferences such as buildings, reefs, and sea clutter in the sea surface area can disturb the detection of the real ship targets. It can be seen from Figure 11b that the top-hat can detect all the real ship targets in different scenarios, while there still exist a large number of false alarms. Moreover, depending on the selected structural elements' size, some detected ship targets may appear incomplete. The K-means, a cluster algorithm with strong spatial correlation, adopting the Euclidean distance between gray values, achieves relatively complete segmentation results, while it generates numerous false alarms in complex areas. Unlike the K-means, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Num.** & \(\mathbf{F_{WH}}\) & \(\mathbf{F_{Z}}\) & \(\mathbf{F_{D}}\) & \(\mathbf{F_{C}}\) & **Judgment Results** \\ \hline (1) & 2.3889 & 36.4018 & 0.8717 & 0.2739 & \(F_{D}>F_{D}\_H\), Non-target \\ (2) & 0.9667 & 40.8619 & 0.8291 & 0.3412 & \(F_{D}>F_{D}\_H\), Non-target \\ (3) & 3.3333 & 23.3506 & 0.8979 & 0.2714 & \(F_{D}>F_{D}\_H\), Non-target \\ (4) & 6.5526 & 226.3472 & 0.5453 & 0.2561 & \(F_{Z}>F_{Z}\_H\), Non-target \\ (5) & 2.9632 & 185.8927 & 0.5574 & 0.2166 & \(F_{Z}>F_{Z}\_H\), Non-target \\ (6) & 2.5098 & 21.7075 & 0.9448 & 0.3397 & \(F_{D}>F_{D}\_H\), Non-target \\ (7) & 5.0800 & 87.2140 & 0.2626 & 0.1184 & \(F_{D}<F_{D}\_L\), Non-target \\ (8) & 19.6000 & 145.2449 & 0.5775 & 0.0737 & \(F_{WH}>F_{WH}\_H\), Non-target \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The feature quantities of the negative samples and judgment results. Figure 10: Eight kinds of disturbing substances and binary slices. the FCM is a fuzzy clustering algorithm, into which membership is introduced to classify different pixels. As is shown in Figure 11d, the FCM is highly sensitive to noise. While targets in relatively flat areas can be separated from the background, this method generates the highest number of false alarms among all the methods. By utilizing merge operations based on the grayscale features of the local area, the mean-shift can reduce false alarms caused by small-scale interferences. However, if the grayscale difference between the ship and the background is small, this method is prone to missed detections and sometimes fails to detect the real targets. The GC, combining the ideas of K-means clustering and background modeling, can perform clustering and determine whether pixels are the target or not by establishing background and foreground models using the Gaussian mixture model. However, manual selection of the candidate area is required, as all the unselected area will be considered as background, and the final segmentation result is dependent on the box selection in scenarios where only parts of the sea surface area, including all the ships, are manually selected. As shown in Figure 11f, the ships in the first scenario were extracted completely, while in other scenarios, some targets were absorbed by the background.