# SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
## 4 Results and Discussion
### Comparison With SAG  
We compared the results with those of an adaptive thresholding algorithm, SAG [18]. The SAG algorithm is based on a combination of the Genetic Algorithm (GA) and Simulated Annealing (SA), which focuses on obtaining adaptive thresholds. The SAG in  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Region/** & **(0, 4]** & **(4, 10]** & **(10, 20]** & **(20, 100]** & **(100, 200]** & **(200, +\(\infty\)]** & **Total** \\ \hline  & **Manual** & **Detected** & **Manual** & **Detected** & **Manual** & **Detected** & **Manual** & **Detected** & **Manual** & **Detected** & **Manual** & **Detected** \\ \hline R1 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 2 & 2 \\ \hline R2 & 4 & 5 & 4 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 8 & 9 \\ \hline R3 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\ \hline R4 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 2 & 2 \\ \hline total & 4 & 5 & 7 & 7 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 13 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of manual counting and detected sunspots in different scales for each region in Figure 6.  
Figure 7: **(a)** A full-disk solar image observed on 4 May 2013 at 8:00 UT. The region at solar limb labeled R1 is magnified four times in **(c)**. **(b)** The detection and segmentation results. **(c,d)** Enlarged views of the origin sunspot regions and segmentation results, respectively. It is clear there are three fault detections at solar limb.  
evitably applies traditional image technologies such as smoothing, morphological operators, and so on.  
Figure 8a shows a full-disk solar image observed by SDO/HMI at 04:00 UT on 24 October 2014, which is very active. Figure 8b shows the detection results obtained by the SAG algorithm, copied from Figure 3 in [PERSON] et al. [18]. The regions from R1 to R9 in yellow boxes are retained for comparison. Figure 8c shows the detection results using our method. The results of SAG show that SAG is very good at the edges of sunspots, but many ultra-small sunspots are missed during detection. The SIPNet & SAHI method has good performance in detecting all-scale sunspots, while the masks of sunspots are explicit.  
Table 3 lists the number of sunspots detected by the SAG algorithm and the SIPNet & SAHI method within different scales for comparison. Sunspots larger than 10 msh are detected equally by both methods. In the range of sunspots smaller than 10 msh, a sunspot within 0 \(\sim\) 4 msh in R3 was incorrectly detected, and a total of 204 ultra-small sunspots within 0 \(\sim\) 4 msh were missed by the SAG method. Compared with the SAG, one sunspot within 4 \(\sim\) 10 msh in R2 and one sunspot within 0 \(\sim\) 4 msh in R4 were falsely detected, and a total of 20 ultra-small sunspots within 0 \(\sim\) 4 msh were missed by the SIPNet & SAHI method. In other words, based on a total of 301 manually counted ultra-sunspots, about 68% of ultra-sunspots were missed by the SAG, while about 6% were missed by the SIPNet & SAHI method. The threshold yielded by the SAG is adaptive and effective, but all thresholding methods face the same issue, where a higher threshold tends to miss sunspots while a lower threshold tends to increase false detections.  
Figure 8: (**a**) A very active solar image observed on 24 October 2014 at 04:00 UT by SDO/HMI. (**b**) The detection results obtained by the SAG algorithm, which is copied from Figure 3 in [PERSON] et al. [18]. The regions from R1 to R9 in yellow boxes are retained for comparison. (**c**) The detection results by SIPNet & SAHI.