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Long-wave infrared (LWIR) hyperspectral imagers (HSI) image a scene by collecting high-resolution spectra at each pixel. The data are similar to a camera image but have a large number of narrow spectral bands rather than the familiar broad three bands of red-green-blue in a traditional digital camera. Materials in LWIR are emissive (rather than reflective) and have unique spectra that can be used for material detection and identification. The measured spectra are a convolution of the material spectra (emissivity), the black body temperature (Planck curve), other interacting environmental spectral sources, and measurement error. One approach to material identification is temperature-emissivity separation (TES), which separates or deconvolves the material spectra from the temperature curve. To accomplish this task, we develop a unique flexible model which combines the mathematical model of the physical processes within a Bayesian nonparametric framework. In addition to offering interpretable estimates of model parameters, this model is able to identify material emissivity spectra and cluster pixels into appropriate material groups. We demonstrate our method using both a synthetic and measured dataset. The online supplementary material contains an appendix of the details of the sampling algorithm. [ABSTRACT FROM AUTHOR]

This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps. [ABSTRACT FROM AUTHOR]