## 5 Conclusions  
In this paper, motivated by previous auroral oval segmentation methods, we propose a novel deep feature-based adaptive level set model to extract the auroral oval region in the UVI images. By utilizing the confidence value obtained from pre-trained CNN, the global energy term is constructed and incorporated into the LIDLSM's energy functional. Additionally, an adaptive time step is introduced to the evolution equation of the level set. The experiments validate that the main contributions of our method are as follows:  
* The comparative experiment between the LIDLSM and DFALS models demonstrate that the proposed method owes stronger robustness to different contour initializations.
* It can be seen from the subjective evaluation experiments that the auroral oval region extracted by DFALS agrees well with the experts' annotated benchmark.
* Compared with the RLSF, SPFLIF-IS, SIIALSM, and LIDLSM models, the DFALS model improves the auroral oval segmentation accuracy in the intensity inhomogeneous region of the UVI images. Furthermore, the DFALS model also obtains the best performance in terms of the boundary- and region-based evaluation metrics.  
The DFALS model focuses on segmenting the UVI images which have complete auroral oval. However, in practice, some aurora in the UVI images are incomplete due to the capturing position of the ultraviolet imager instrument aboard on the Polar satellite. In the future, we will consider adaptively setting the weight coefficients and introducing the prior knowledge of the auroral shape, so that the incomplete aurora can also be segmented accurately.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/7/2590/s1](http://www.mdpi.com/2076-3417/10/7/2590/s1)]([http://www.mdpi.com/2076-3417/10/7/2590/s1](http://www.mdpi.com/2076-3417/10/7/2590/s1)), Figure S1: The contour initializations of different methods when segmenting the first UVI image in Section 4.3.1. **(a)** Initial level set curves (concentric circles) of DFALS. **(b)** Initial level set curves (LLS-RHT result) of SIIALSM and LIDLSM. Figure S2: The contour initializations of different methods when segmenting the second UVI image in Section 4.3.1. **(a)** Initial level set curves (concentric circles) of DFALS. **(b)** Initial level set curves (LLS-RHT result) of SIIALSM and LIDLSM. Figure S3: The contour initializations of different methods when segmenting the third UVI image in Section 4.3.1. **(a)** Initial level set curves (concentric circles) of DFALS. **(b)** Initial level set curves (LLS-RHT result) of SIIALSM and LIDLSM.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived the algorithm and designed the experiments; [PERSON] implemented the experiments; [PERSON] analyzed the results; and [PERSON] drafted the manuscript, [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61473310, 41174164.  
**Acknowledgments:** The authors would like to thank NASA's Space Physics Data Facility (SPDF) for providing UVI images from the Polar satellite, which is available at [[https://cdaweb.gsfc.nasa.gov/cgi-bin/eval3.cgi](https://cdaweb.gsfc.nasa.gov/cgi-bin/eval3.cgi)]([https://cdaweb.gsfc.nasa.gov/cgi-bin/eval3.cgi](https://cdaweb.gsfc.nasa.gov/cgi-bin/eval3.cgi)).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **RLSF** & **SPFLIF-IS** & **SIIALSM** & **LIDLSM** & **DFALS** \\ \hline Mean value & 31.173 & 33.927 & 33.002 & 25.484 & **16.565** \\ Standard deviation & 6.609 & 6.9569 & 17.729 & 6.610 & **3.560** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean value and standard deviation of \(P_{mp}\) given by different methods on test images in %.