## 4 Experiment and Results
### Comparison with Other Methods
#### 4.3.2 Objective Evaluation  
To objectively evaluate the segmentation accuracy of different methods, an experiment was conducted to compare the segmentation results with the benchmark on 150 test images. Both boundary-based and region-based measures are employed.  
* Boundary-based measurement Pixel deviation \(P_{d}\) and gap pixel percentage \(P_{g}\)[19] are adopted as the evaluation metrics in the Boundary-based measurement. \(P_{d}\) is a summative measure of the distance variation of the automatically  
Figure 12: The segmentation results of different methods. (**a**) UVI image captured by polar satellite on Jan 10 1997 01:16:40. (**b**) The benchmark annotated by experts manually. (**c**) The segmentation result of RLSF. (**d**) The segmentation result of SPFLIF-IS. (**e**) The segmentation result of SIIALSM. (**f**) The segmentation result of LIDLSM. (**g**) The segmentation result of DFALS.  
Figure 13: The segmentation results of different methods. (**a**) UVI image captured by polar satellite on Jan 10 1997 03:10:08. (**b**) The benchmark annotated by experts manually. (**c**) The segmentation result of RLSF. (**d**) The segmentation result of SPFLIF-IS. (**e**) The segmentation result of SIIALSM. (**f**) The segmentation result of LIDLSM. (**g**) The segmentation result of DFALS.  
segmented boundary from the expert's annotated boundary. \(P_{g}\) indicates the proportion of the gap in the whole auroral oval boundary. The smaller these two metric values, the better the segmentation result. \(P_{d}\) and \(P_{g}\) are calculated as follows:  
\[P_{d}=\frac{\sum_{b_{a}\in B_{a}}d(b_{a},B_{b})+\sum_{b_{b}\in B_{b}}d(b_{b},B_{ a})}{N_{a}+N_{b}} \tag{12}\]  
\[P_{g}=\frac{N_{gap}}{N_{b}}\times 100\% \tag{13}\]  
where \(B_{a}\) and \(B_{b}\) are the pixel sets on the segmented auroral oval boundaries and the benchmark boundaries. \(b_{a}\) and \(b_{b}\) are pixels in \(B_{a}\) and \(B_{b}\), respectively. \(N_{a}\) and \(N_{b}\) are the number of elements in set \(B_{a}\) and \(B_{b}\), respectively, and \(N_{gap}\) denotes the number of gap pixels. \(d(b_{1},B_{2})=\underset{b_{2}\in B_{2}}{min}b_{1}-b_{2}\) represents the minimum distance from pixel \(b_{1}\) to the pixels in set \(B_{2}\).  
Tables 1 and 2 list the mean value and standard deviation of \(P_{d}\) and \(P_{g}\) given by different methods. The best results are labeled in bold. For these two metrics, the RLSF model performs slightly worse than the SPFLIF-IS model, which reflects that the problem of auroral oval segmentation is mainly brought by intensity inhomogeneity. Besides, the suboptimal \(P_{g}\)'s mean value of SPFLIF-IS shows that this method can efficiently inhibit the boundary leakage in the low contrast region in the UVI images. Both initialized by LLSRHT result, the comparison between the LIDLSM and the SIIALSM verifies the effectiveness of the shape energy term in the LIDLSM. Though the \(P_{d}\)'s mean value of the LIDLSM is suboptimal, this method owes the largest value of standard deviation except for SIIALSM. The large \(P_{d}\)'s standard deviation of the SIIALSM and LIDLSM is mainly brought by some poor LLSRHT result initializations, which also indirectly reflects that these two methods are sensitive to contour initializations.  
methods, the standard deviation of \(P_{d}\) and \(P_{mp}\) obtained by the SIIALSM are the largest, which shows that this method is sensitive to different auroral oval shapes and contour initializations. The auroral oval region extracted by the DFALS model agrees well with the expert annotated auroral region which can be verified from the lowest \(P_{mp}\)'s mean value and standard deviation. Indeed, the DFALS model obtains the lowest value of all three metrics. From Table 3, we can conclude that with the help of deep feature-based global energy term, the auroral oval segmented by the DFALS matches well with the benchmark annotated by the experts.