Longwave Infrared Hyperspectral Emissivity Retrieval With Robustness to Spectral Uncertainty

Exploitation of hyperspectral long-wave infrared (LWIR) data often involves an emissivity retrieval process where ground-surface emissivity is estimated from at-sensor radiance measurements. Emissivity retrieval consists of both atmospheric compensation and temperature separation, however, the presence of spatial–spectral distortion or small spectral calibration errors can engender large errors in emissivity retrieval. In this article, we propose an efficient emissivity retrieval algorithm that jointly performs atmospheric compensation and temperature-emissivity separation while compensating for spectral uncertainty that may exist due to smile or calibration artifacts. Without relying on particular in-scene materials, such as blackbodies or in-library elements, the proposed algorithm estimates unique spectral response function (SRF) band centers and bandwidths at each spatial-spectral index of the focal plane array (FPA) in order to compensate for optical distortions. Like the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) method, the approach utilizes a spectral smoothness form of regularization. The proposed algorithm, decoding infrared atmospheres in ARTEMISS (DIANA), relies on a deep neural network decoder in order to efficiently model a sequence of SRF-weighted transmission-upwelling-downwelling (TUD) vectors applicable across the FPA and employs a genetic algorithm to perform the high-dimensional inference task. Experimental results on synthetic and measured data quantify performance in a range of environmental conditions and distortion levels, while results from measured data highlight a 26%–31% average reduction in emissivity estimation error relative to existing algorithms.