# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 2 Materials and Methods
### Method
#### 2.3.2 Multi-Feature Extraction
The data preprocessing and multi-feature extraction processes in this study, including calculations of the radiance, brightness temperature, thermal radiation indices, and optical indices, were implemented using code written in Python. Industrial Heat Source Production Area Identification Model Based on SVM  
Due to SVM's \"small sample advantage\" [52], it was introduced to construct an IHSPA identification model. First, sample points (IHSPA samples and non-IHSPA samples) based on Google Earth images and SDGSAT-1 satellite brightness temperature data were constructed. The IHSPA samples were labeled as positive, indicating areas within factory boundaries exhibiting significantly higher thermal radiation anomalies. Non-IHSPA samples, marked as negative, refer to locations that might resemble factory structures but do not show elevated thermal radiation anomalies or areas with high thermal radiation and that, upon inspection via Google Earth imagery, are not factories, potentially misleading the model identification. Then, the multi-features described in Section 2.3.2 of the sample points were extracted. Second, an IHSPA identification model based on SVM was constructed using sample data. This stage was divided into the following steps. Initially, samples characterized by multi-features were input, followed by their random division into training and testing samples at a ratio of 8:2, with both sets maintained strictly independent from one another. Test sample points were used to assess the reliability and quality of the IHSPA identification model. Subsequently, the built-in SVM model on MATLAB was employed for training. The adjustment of the RBF kernel's cost and gamma parameters was necessary when utilizing SVM. The GridSearch method, widely applied and suitable for optimizing multiple parameters simultaneously, was utilized in this study. The tuning of the cost and gamma was facilitated by setting step sizes, enabling the systematic optimization of the parameters within specified ranges. The values of the cost were \(2^{\text{cost}_{\text{min}}},\ 2^{\text{cost}_{\text{min}+\text{step}}},\ \ldots,\ 2^{\text{cost}_{\text{max}}}\), and the values of the gamma were \(2^{\text{gamma}_{\text{min}}},\ 2^{\text{cost}_{\text{min}+\text{step}}},\ \ldots,\ 2^{\text{gamma}_{\text{max}}}\). In this study, \(\text{step}=1\) and \(\text{gstep}=1\) were set as the step values, with \(\text{cost}_{\text{min}}\) and \(\text{gamma}_{\text{min}}\) established at \(-3\), and \(\text{cost}_{\text{max}}\) and \(\text{gamma}_{\text{max}}\) established at 8. The approach ensured the identification of the optimal parameters. Finally, the best parameters and training samples were utilized to train an SVM model for the identification of IHSPAs. Then, IHSPAs in the study area were identified based on the IHSPA identification model. Muti-feature data within the study area were inputted, and the model output a binary raster map, with 0 indicating an IHSPA and 1 denoting a non-IHSPA. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Pixels with three consecutive values of 1 were considered as an IHS object, and Google imagery was overlaid as a base map to construct the boundaries of the IHS. The constructed IHS objects were then manually verified to confirm their authenticity as real IHSs.