Temperature and emissivity separation via sparse representation with thermal airborne hyperspectral imager data

Abstract

The thermal airborne hyperspectral imager (TASI), which has 32 channels that provide continuous spectral coverage within wavelengths of 8 to 11.5 μm, is very beneficial for land surface temperature and land surface emissivity (LSE) retrieval. In remote sensing applications, emissivity is important for features classification and temperature is important for environmental monitoring, global climate change, and target recognition studies. This paper proposed a temperature and emissivity separation method via sparse representation (SR-TES) with TASI data, which employs a sparseness differences point of view whereby the atmospheric spectrum cannot be considered SR under the LSE spectral dictionary. We built the dictionary from Johns Hopkins University’s spectral library as an overcomplete base, and the dictionary learning K-SVD algorithm was adopted. The simulation results showed that SR-TES performed better than the TES algorithm in the case of noise impact, and the results from TASI data for the Liuyuan research region were reasonable; partial validation revealed a root mean square error of 0.0144 for broad emissivity, which preliminarily proves that this method is feasible.