## 4 Experiment and Results
### Comparison with Other Methods
#### 4.3.1 Subjective Evaluation  
From test images for validating the DFALS model, we choose three auroral oval images that are difficult to segment for comparison experiments. In the experiments, the inner and outer level set curves of the DFALS model are initialized by two concentric circles, whose radius is 15 and 98, respectively. The center of concentric circles coincides with the image center. The contour initializations of different methods for these three auroral oval images are shown in Figures S1-S3.  
As shown by the red rectangle in Figure 11a, there exist dayglow contamination regions in the first UVI image. The auroral oval annotated by experts and segmentation results obtained by different methods automatically are also shown in Figure 11b-f. The RLSF, SPFLIF-IS, and SIIALSM models mistakenly identify the edge of UVI's lens as the outer boundary of the auroral oval. In contrast, LIDLSM and DFALS obtain reasonable segmentation results. Compared with the LIDLSM, the DFALS model shows superiority on retaining the details of the inner boundary of the auroral oval, as shown by the blue rectangle in Figure 11f,g.  
Figure 10: The segmentation results of LIDLSM and the deep feature-based adaptive level set (DFALS) model initialized by several different level set curves. (**a**) The UVI images captured by Polar satellite on Jul 31 1997 01:34:00 and Jul 31 1997 01:47:23. (**b**) The initial level set curves. (**c**) Segmentation results of the DFALS. (**d**) Segmentation results of the LIDLSM.  
The second auroral oval image contains low contrast region as marked by the red rectangle in Figure 12a. As aforementioned, the RLSF model improved the segmentation result when the image's noise distribution is unknown and the SPFLIF-IS model was developed to address the segmentation problem brought by image intensity inhomogeneity. However, these two methods fail to obtain accurate segmentation results. The RLSF model eliminates the noise point in the segmentation result while missing to extract complete auroral oval in the low contrast region. The SPFLIF-IS model inhibits apparent boundary leakage of the auroral oval in the low contrast region yet regarding many noise pixels as the auroral oval region. Influenced by the intensity inhomogeneity, the segmentation results of SIIALSM and LIDLSM also breaks in the low contrast region. Compared with other methods, the DFALS model obtains the most reasonable segmentation result with the help of deep feature-based global energy term.  
The third auroral oval image also includes low-contrast regions around the boundary, which is marked with the red box in Figure 13a. As seen from the segmentation results, the contours obtained by RLSF and SIIALSM break in the low contrast region, and SPFLIF-IS mistakes the noise pixels as part of the auroral oval. By comparing Figure 13b,f, we can conclude that although the boundary leakage does not appear in the segmentation result of LIDLSM, the extracted auroral oval deviates from the benchmark significantly in the low contrast region. However, regardless of the intensity inhomogeneity and image noise, DFALS yields the segmentation result that most closely matches the benchmark.  
Figure 11: The segmentation results of different methods. (**a**) UVI image captured by the Polar satellite on Jul 31 1997 09:34:48. (**b**) The benchmark annotated by experts manually. (**c**) The segmentation result of the region-based active contour model via local similarity factor (RLSF). (**d**) The segmentation result of the improved signed pressure force and local image fitting based image segmentation (SPFLIF-IS) method. (**e**) The segmentation result of the shape-initialized and intensity-adaptive level set method (SIIALSM). (**f**) The segmentation result of LIDLSM. (**g**) The segmentation result of DFALS.