An automatic extraction method for geothermal radiation sources based on an LST retrieval algorithm and semantic network

Geothermal resources are efficient, renewable and clean energy sources, and their reservoirs are usually closely associated with high-temperature regions of the land surface. Current exploration methods primarily involve migrating traditional geological techniques, which fail to fully use the unique features of geothermal radiation characteristics. Thermal infrared remote-sensing imaging technology can capture and present areas with distinctive surface thermal radiation features, providing considerable significance as a guide for localization prior to field exploration. In this study, we propose a deep learning–based method for intelligently identifying and segmenting geothermal radiation sources from thermal infrared remote-sensing images, including data preparation and model training. To improve the localization drift and anomalous interference caused by the high complexity of the Earth's surface environment, this study uses a surface temperature retrieval algorithm to calculate the land surface temperature in the research area. The retrieval results are used to train the semantic segmentation model. In addition, a pixel-level geothermal spatial segmentation network (PGSSNet) is proposed to suppress the diffuse thermal radiation and reduce the broad and blurred white areas of images to exact locations. Once the training is completed, the model directly segments and extracts the actual range of thermal radiation sources from subsequent thermal infrared remote-sensing images without temperature retrieval and/or manual calibration.