## 4 Results and Analysis  
Considering the particularity of the scene in this work and combining it with the existing literature, existing evaluation indexes are adopted and improved in this work to objectively evaluate the recognition accuracy of underwater targets. TP (True Positive), FP (False Positive), TN (True Negative), and FN (False Negative) are assumed as four indexes calculated from the confusion matrix, which can be used to calculate the False Negatives Rate (FNR) and the False Positive Rate (FPR). FNR represents the ability of the target detection model to correctly predict the purity of negative samples and reduces the prediction of positive samples as negative samples, that is, the proportion of positive samples predicted as negative samples in the total positive samples. The smaller the FNR value, the better the target detection performance. \[FNR=\frac{FN}{P} \tag{19}\]  
FPR represents the ability of the target detection model to correctly predict the purity of positive samples, and reduces the prediction of negative samples to positive samples, that is, the proportion of negative samples predicted as positive samples to the total number of negative samples. The smaller the FPR value, the better the target detection performance. \[FPR=\frac{FP}{N} \tag{20}\]  
The Signal-to-Clutter Ratio (SCR) is defined in Equation (21), where the higher the SCR for the target, the easier it will be detected. \[SCR=\frac{\left|\mu_{t}-\mu_{b}\right|}{\sigma_{b}} \tag{21}\]  
where \(\mu_{t}\) is the average pixel value of the target, \(\mu_{b}\) and \(\sigma_{b}\) are the average pixel value and the standard deviation value of the pixel value for the adjacent area of the target, respectively. Since the area of background is much larger than that of target in the research image, the number of the background and the target samples will be unbalanced, (i.e., the positive and negative samples are unbalanced), when all the background areas are used in accuracy evaluation, which reduces the efficiency of evaluating SCR and ROC (Receiver Operating Characteristic). In order to reduce the impact, the target window and background window are set to 20 \(\times\) 20 pixels and 40 \(\times\) 40 pixels, respectively, as shown in Figure 5. The resetting of the calculation area is just for the comparison of the performance of the algorithms. Figure 5: Diagram of the target and background window size setting involved in SCR and ROC calculation. In general, the FNR and FPR are global indicators, while the improved SCR and ROC in our work belong to local indicators. Therefore, the evaluation system in this work is reasonable by comprehensively evaluating the target detection accuracy from both the global and local areas. In order to verify the performance and advantages of the proposed algorithms of underwater target detection, seven target detection algorithms for hyperspectral images are compared and analyzed, including Kernel Isolation Forest (KIF) [30], Visual Attention and Background Subtraction (VABS) [31], Sub-Features Grouping and Binary Accumulation (SFBA) [32], Spectral-Spatial Fusion Anomaly Detection Method (SSFAD) [33], Global Reed-Xiaoli (GRX) [34], Local Reed-Xiaoli (LRX) [35] and Low Rank and Sparse Representation (LRASR) [36]. Table 2 shows the comparison results of the precision in different algorithms. Figures 6-9 show the results of experiments 1 to 4, respectively. As can be seen from Figure 9b-i, the background regions of KIF, SFBA and SSFAD are much rougher than those of the algorithms in our work, including VABS, GRX, LRX and LRASA. Figure 6b-i show that the anomalous points detected by some algorithms are located at the central region of the target, while in some other algorithms the points are located at the edge region. For experiment 1, multiple peaks appear in both the target and background regions of SFBA, with the peak values of the target region lower than those in the background region, which leads to the misdetection in experiment 1.