# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 4 Discussion  
### Comparison with Existing IHS Data  
To generate industrial heat source objects from IHSPAs, a manual industrial heat source object identification method using high-resolution remote sensing images was adopted. Then, SDGSAT-1 TIS and the combination of multi-feature extraction and SVM were used to identify 371 IHS objects from the IHSPAs results. The 339 objects were verified as real IHSs via multiple manual validations that used Google Earth images (0.5 m). The identification accuracy was 91.37% (339/371). To compare and analyze the effectiveness of our new model, our results were compared with [PERSON]'s inventory based on active fire point data [21] and [PERSON]'s inventory based on night fires [3]. A total of 66 and 44 IHSs were obtained from [PERSON] (2018) and [PERSON] (2018), respectively. In addition, 65 (98.48%) and 43 (97.73%) objects, respectively, were verified as real IHSs via multiple manual validations. Although the recognition accuracy of our results was lower than those of the [PERSON] and [PERSON] inventories, our results identified many more industrial heat source objects. Our method is the only method that can detect IHSPAs precisely. The average area, minimum area, maximum area, and total area of industrial heat source objects were statistically analyzed from our results and [PERSON]'s and [PERSON]'s inventories. The final statistical results are shown in Table 7. Figure 4 shows the spatial distributions of our results and those of [PERSON]'s and [PERSON]'s inventories. From Table 7 and Figure 4, some conclusions can be obtained:  
1. The number of our results was 5.22 times greater than the number of [PERSON]'s inventory and 7.88 times greater than that of [PERSON]'s inventory. This suggests that our method, due to improvements in the thermal infrared data resolution and sensitivity to low-temperature objects, may identify a greater number of factories with smaller scales and lower levels of thermal radiation;
2. The total area of IHSs identified by our study was 666.89 km\({}^{2}\), which was 332.08% and 286.99% more than the areas identified by [PERSON] and [PERSON] (200.82 km\({}^{2}\)/232.37 km\({}^{2}\)), respectively. At the same time, the minimum identification area of our results was 0.03 km\({}^{2}\), rather than the 0.16 km\({}^{2}\) and 0.26 km\({}^{2}\) of [PERSON]'s and [PERSON]'s inventories, respectively. This means that the IHSs based on our method were granular with greater precision. It improved the phenomenon of \"one industrial heat source object covering multiple factories and mines\"; \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Feature** & **T1** & **NDBI** & **RI2** & **RI3** & **NDWI** & **T2** & **NDVI** & **T3** & **RI4** & **RI1** \\ \hline Weight & 2.22 & 2.09 & 2.00 & 1.61 & 1.51 & 1.16 & 0.89 & 0.27 & 0.43 & 0.14 \\ \hline \end{tabular}
\end{table} 
Table 6: Feature importance weights based on NCA. NCA methods were used to evaluate the importance weights of 10 different features to identify IHSPAs. The importance results were ranked in descending order of weight values. Higher weight values indicate greater importance. The spatial coverages of our results for [PERSON]'s and [PERSON]'s inventories were 93.85% (61/65) and 79.55% (35/44), respectively, which were higher than the spatial coverages of [PERSON]'s and [PERSON]'s inventories for our results (74.67% and 62.14%, respectively). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **True** & **False** & **Total Number** & **Accuracy** & \begin{tabular}{c} **Average Area** \\ **(km2)** \\ \end{tabular} & \begin{tabular}{c} **Minimum Area** \\ **(km2)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum Area** \\ **(km2)** \\ \end{tabular} &
\begin{tabular}{c} **Total Area** \\ **(km2)** \\ \end{tabular} \\ \hline Our result & 339 & 32 & 371 & 91.37\% & 1.80 & 0.03 & 68.95 & 666.89 \\ [PERSON]’s data & 65 & 1 & 66 & 98.48\% & 3.04 & 0.16 & 52.52 & 200.82 \\ [PERSON]’s data & 43 & 1 & 44 & 97.73\% & 5.28 & 0.26 & 28.37 & 232.37 \\ \hline \hline \end{tabular}
\end{table}