Second, we carried out a simulation and inversion experiment and analyzed the relationship between instrument spectral resolution, noise level, the ARTEMISS parameter setting and the retrieval errors separately. First, we analyzed the influence mechanism of noise on the retrieval errors of ARTEMISS in theory. In this study, we selected the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) algorithm-the representative of the iterative spectral smoothness temperature-emissivity separation algorithm family-as the research object and proposed an improved algorithm. Although there is a lot of research regarding the influence of noise on retrieval errors, few studies have focused on the mechanism. The algorithms are sensitive to a number of factors, where noise is difficult to handle due to its unpredictability. There are numerous algorithms that can be used to retrieve land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (HTIR) data. Finally, we present the results of an extensive experimental analysis carried out over simulated data to assess and compare the performance of the two presented algorithms. Furthermore, by specifying two basis matrices for the emissivity subspace, we propose two different algorithms within the SBTES class. We study the performance of the presented class of algorithms and derive theoretical bounds on the accuracy of the temperature and emissivity estimators. The proposed approach originates several algorithms whose specific form depends on the particular basis matrix adopted to address the emissivity subspace. Specifically, by exploiting the subspace representation and the Gaussian model for the noise affecting LWIR hyperspectral data, we approach TES under a statistical perspective by obtaining the maximum likelihood estimates of both the temperature and the spectral emissivity. We derive a general class of TES algorithms relying on the assumption that the emissivity spectra of natural and man-made materials can be well represented in a given subspace of the original data space. In this paper, we investigate the temperature and emissivity separation (TES) problem from hyperspectral data acquired in the long-wave infrared region (LWIR) of the electromagnetic spectrum.
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