LAUSR

With the popular use of remote sensing techniques, investigations into hyperspectral system designs and parameter trade-off studies have become more and more necessary. Analytical models based on statistical descriptions and energy propagation are certainly efficient methods to examine a large number of parameter trades and sensitive studies with low computational cost. In long wave Infrared (LWIR), an analytical version of a temperature/emissivity separation (TES) algorithm can be used to retrieve ground emissivity statistics. However, such a statistical analytical algorithm has not been fully developed, as far as we know. In this letter, a new statistical iterative spectrally smooth temperature/emissivity separation (S-ISSTES) algorithmic approach is proposed. The derivation and comparison of our statistical approach is discussed in detail. We show that it can retrieve first- and second-order statistics of surface spectra as well as the associated temperature from at-sensor radiance data. Experimental results using both real and synthetic data demonstrate the effectiveness of the proposed S-ISSTES algorithm.