Hyperspectral images are made up of energy measurements at different wavelengths of light. The case is considered where these measurements are dependent on temperature, the self-emitted energy (emissivity), and reflected energy (downwelling radiance) from the surroundings. The process where the downwelling radiance is fixed and the temperature and emissivity are estimated is referred to as temperature/emissivity separation. Due to the way these terms mix, for a given set of measurements, there exist many pairs of temperatures and emissivities that satisfy the model. This creates ambiguity in the solution that must be resolved for the result to have any significance. A new model is developed which reduces this ambiguity. This model is used to form an objective function. The temperature and emissivity which maximize the value of the objective function are solved for given a set of measurements. As part of the solution, a new algorithm is developed which exploits the shape of the objective function to estimate the temperature and emissivity quickly and accurately. Extensive testing of this algorithm is performed to gain an understanding of its average speed and accuracy.

In conventional remote sensing, visible-near infrared (VNIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum (EMS) have been utilized for lithological discrimination extensively. Additionally, TIR part of the EM spectrum can also be utilized for discrimination of surface materials either through emissivity characteristics of materials or through radiance as in VNIR and SWIR. In this study, ASTER thermal multispectral infrared data is evaluated in regard to lithological discrimination and mapping through emissivity values rather than conventional methods that utilize radiance values. In order to reach this goal, Principle Component Analysis (PCA) and Decorrelation Stretch techniques are utilized for ASTER VNIR and SWIR data. Furthermore, the spectral indices which directly utilize the radiance values in VNIR, SWIR and TIR are also included in the image analysis. The emissivity values are obtained through Temperature-Emissivity Separation (TES) algorithm. The results of the image analyses, except spectral indices, are displayed in RGB color composite along with the geological map for visual interpretation. The results showed that utilizing emissivity values possesses potential for discrimination of organic matter bearing surface mixtures which has not been possible through the conventional methods. Additionally, PCA of emissivity values may increase the level of discrimination even further. Since the emissivity utilization is rather unused throughout in literature and new, further assessment of accuracy is highly recommended along with the field validations.