Improvement of Temperature and Emissivity Separation Algorithm for Thermal Infrared Hyperspectral Imaging Based on Airborne Data

Temperature and emissivity separation (TES) is a crucial process for converting thermal infrared (TIR) hyperspectral data into actionable information. Since the development of thermal infrared hyperspectral imagers is still in its nascent stage, most TES algorithms have been validated primarily using simulated data within the context of land resource remote sensing. However, there has been insufficient focus on urban environments with complex underlying surfaces, particularly on low emissivity targets. In this study, we developed a TES algorithm capable of adapting to a broader emissivity range. The performance of the algorithm in retrieving temperature and emissivity was evaluated against several typical TES algorithms, using data cubes acquired by the airborne thermal infrared hyperspectral imaging system (ATHIS) as test data. The experimental results revealed that existing algorithms exhibited relatively high retrieval errors for low emissivity ground objects. In contrast, the developed algorithm significantly improved the accuracy of retrieval for such objects while maintaining comparable accuracy for non-low-emissivity targets. These findings suggest that the proposed algorithm enhances TES accuracy and expands the applicability of thermal infrared hyperspectral imaging in environmental remote sensing of urban environments with complex underlying surfaces.