Object parameters optimization on pure and mixed pixels in thermal hyperspectral imagery

The hyperspectral image in thermal infrared domains provide information, such as temperature and emissivity, about different kinds of materials. These information can be used for a wide number of applications such as mineral mapping, bathymetry, indoor and outdoor detection of chemicals. But because of the limitation of spatial resolution and the characteristics of thermal infrared sensor, there are many mixed pixels in the data, whose temperature emissivity and abundance of different components can be hard to estimate. In this paper, a new method to estimate the parameters in pure and mixed pixels is proposed based on linear and nonlinear optimization. Firstly, the standard temperature and emissivity separation (TES) algorithm is applied on pure pixels of different materials selected by supervise or unsupervised methods to get the initial temperature. Secondly, the emissivity in different bands can be retrieved by minimizing the reconstruction error, which the more accurate temperature is optimized with. The emissivity in one band is trained by the samples in the same band but in different pixels, while the temperature is trained by different bands in one pixel. Lastly, the abundance and temperature of components in mixed pixels are estimated based on a linear mixture model of the bottom of atmosphere radiance as full constraint linear optimization problem and nonlinear optimization problem. The method is also analyzed with respect to sensitivity to the noise and different parameters' influences on estimation errors.