Paper Info
Title
A Statistical Temperature Emissivity Separation Algorithm for Hyperspectral System Modeling
Abstract
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.
Year
DOI
Venue
2022
10.1109/LGRS.2022.3140754
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Temperature sensors, Land surface temperature, Temperature distribution, Analytical models, Reactive power, Surface treatment, Prediction algorithms, Forecasting and analysis of spectroradiometric system performance (FASSP), hyperspectral, long wave Infrared (LWIR), remote sensing, statistical iterative spectrally smooth temperature, emissivity separation (S-ISSTES), statistical modeling, temperature, emissivity separation (TES)
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Runchen Zhao100.34
Emmett J. Ientilucci294.44
Peter Bajorski300.34