# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 3 Results  
### Comparative Performance of Different Features and Identification Models on IHSPA Detection  
In this study, we tested different feature combinations for identifying IHSPAs to demonstrate the effectiveness of combining thermal and optical features. Five tests were conducted using Landsat-8 optical features (LOFs), Landsat-8 temperature features (LTFs), SDGSAT-1 thermal features (S-1 TIFs), Landsat-8 optical and temperature features (LOFs & TFs), and Landsat-8 optical and SDGSAT-1 thermal infrared features (LOFs & S-1 TIFs) within the unified framework. The Landsat-8 optical features consisted of the NDVI, NDWI, and NDBI. Landsat-8 temperature features were derived from the temperature of Landsat-8 Band 10. The specific combination of features is shown in Table 4.  
To verify the effectiveness of identifying IHSPAs based on SVM, three comparison machine learning classifiers, decision tree (DT) [53], k-nearest neighbor (KNN) [54], and naive Bayes (NB) [55], were selected in this study. These accuracies were obtained for each classifier under the conditions of the corresponding attributes of the samples, optimal classifier parameters, and ten times tenfold cross-validation.  
Table 5 and Figure 3 show the accuracy and visual results obtained from different features and identification models for IHSPA detection. According to the results in Table 5, LOFs & S-1 TIFs had the highest identification accuracy compared to the other features. SDGSAT-1 thermal infrared features (S-1 TIFs) also achieved high accuracy compared with the LOFs, LOFs & TFs, and LTFs. LOFs & S-1 TIFs and S-1 TIFs can distinguish IHSPAs from the background much more clearly (as shown in Figure 3). Additionally, four classifiers were assessed, and the IHSPA identification based on SVM produced the most accurate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Abbreviation** & **Full Name** & **Number of Features** & **Features** & **Feature Source Data** \\ \hline \multirow{2}{*}{LOFs} & Landsat-8 & \multirow{2}{*}{3} & NDVI, NDBI, NDWI & Landsat-8 OLI data \\  & optical features & & & \\ \hline \multirow{2}{*}{LTFs} & Landsat-8 & \multirow{2}{*}{1} & \multirow{2}{*}{T\({}_{\text{ Landsat-8}}\)} & Landsat-8 OLI data \\  & temperature features & & & \\ \hline \multirow{2}{*}{S-1 TIFs} & SDGSAT-1 & \multirow{2}{*}{7} & \multirow{2}{*}{T\({}_{1}\), T\({}_{2}\), T\({}_{3}\), RI\({}_{1}\), RI\({}_{2}\), RI\({}_{3}\), RI\({}_{4}\)} & SDGSAT-1 TTS data \\  & thermal features & & & \\ \hline \multirow{2}{*}{LOFs \& TFs} & Landsat-8 optical and & \multirow{2}{*}{4} & NDVI, NDBI, NDWI, T\({}_{\text{ Landsat-8}}\) & Landsat-8 OLI data \\  & temperature features & & & \\ \hline \multirow{2}{*}{LOFs \& S-1 TIFs} & Landsat-8 optical & \multirow{2}{*}{10} & NDVI, NDBI, NDWI, T\({}_{1}\), T\({}_{2}\), & SDGSAT-1 TTS data and \\  & features and SDGSAT-1 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different combination methods of features. Five combination models were created using SDGSAT-1 TIS data and Landsat-8 OLI data to demonstrate the effectiveness of our multi-features. LOFs (Landsat-8 optical features), LTFs (Landsat-8 temperature features), and S-1 TIFs (SDGSAT-1 thermal features) represent the use of only one type of feature (optical features or thermal features) and one remote sensing dataset (SDGSAT-1 TIS data or Landsat-8 OLI data). LOF & TF (Landsat-8 optical and temperature features) combined optical and temperature features and used Landsat-8 OLI data. LOFs & S-1 TIFs (Landsat-8 optical features and SDGSAT-1 thermal features) are the multi-features produced in this paper.