Performance limitations of temperature–emissivity separation techniques in long-wave infrared hyperspectral imaging applications

Pieper, Michael; Manolakis, Dimitris G.; Truslow, Eric; Cooley, T.; Brueggeman, M.; Jacobson, J.; Weisner, Andrew

doi:10.1117/1.oe.56.8.081804

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…It is worth pointing out that TES algorithms work in an iterative manner, and are influenced in a complex way by many variables, such as the atmosphere composition, its temperature profile, the sensor spectral resolution and radiometric sensitivity, the spectral signature and temperature of the materials, etc. (Li, Z. L. et al, 1999), (Pieper, M. et al, 2017), (Ahlberg, J. 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, for the materials of interest, not always the minimum value of the rmse, given by the algorithm, corresponds to a minimum value of the temperature mean error, for the same algorithm. As shown in (Pieper, M. et al, 2017), different values of temperature bring to different performances of the algorithms, since the Planck's function moves its emission peak according to the Wien's displacement law. Distinct positions of the peak change the initial conditions of the algorithms.…”

Section: Ideal Casementioning

confidence: 99%

“…In fact, the different TES strategies have been derived starting from different assumptions, and using different approximations/simplifications, strictly related to the characteristics of the sensor (radiometric, spatial, and spectral resolution), and to the spectrum of materials searched in the scene. As shown in (Li, Z., 1999), and recently in (Pieper, M. et al, 2017), the performance prediction of various TES algorithms represents a challenging problem, since the literature lacks a precise methodology, aimed to exploit a common dataset, trials, and indexes, in order to assist in the choice of the proper TES algorithm during the planning of a remote sensing mission. The choice of the most suitable algorithm is critical because the accuracy of the technique affects the performance of the subsequent processing procedures, such as detection, unmixing, and classification.…”

Section: Introductionmentioning

confidence: 99%

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Temperature – Emissivity Separation Assessment in a Sub-Urban Scenario

Moscadelli

Diani

Corsini

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, a methodology that aims at evaluating the effectiveness of different TES strategies is presented. The methodology takes into account the specific material of interest in the monitored scenario, sensor characteristics, and errors in the atmospheric compensation step. The methodology is proposed in order to predict and analyse algorithms performances during the planning of a remote sensing mission, aimed to discover specific materials of interest in the monitored scenario. As case study, the proposed methodology is applied to a real airborne data set of a suburban scenario. In order to perform the TES problem, three state-of-the-art algorithms, and a recently proposed one, are investigated: Temperature-Emissivity Separation '98 (TES-98) algorithm, Stepwise Refining TES (SRTES) algorithm, Linear piecewise TES (LTES) algorithm, and Optimized Smoothing TES (OSTES) algorithm. At the end, the accuracy obtained with real data, and the ones predicted by means of the proposed methodology are compared and discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Ideal Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temperature – Emissivity Separation Assessment in a Sub-Urban Scenario

Moscadelli

Diani

Corsini

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…There are many impact factors to the performance of a TES algorithm for HTIR data, mainly including (1) sensor-related parameters (e.g., sensor altitude, spatial resolution, spectral range, spectral resolution and instrument noise), (2) data pre-processing (before TES) related residual errors (e.g., radiation calibration, spectral calibration and atmospheric correction) and (3) the algorithm's limitations (e.g., the adopted assumptions or constraints) [37][38][39][40][41][42][43][44][45][46][47][48]. The sensor altitude affects the amount of atmospheric radiation entering the sensor.…”

Section: Introductionmentioning

confidence: 99%

“…The sensor altitude affects the amount of atmospheric radiation entering the sensor. The higher the sensor altitude, the greater the inversion error [37,44]. If the spatial resolution is low, non-isothermal mixed pixels will appear in the thermal infrared hyperspectral image, which makes it difficult to separate the temperature emissivity accurately [39,41,42].…”

Section: Introductionmentioning

confidence: 99%

Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness

et al. 2020

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There are numerous algorithms that can be used to retrieve land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (HTIR) data. The algorithms are sensitive to a number of factors, where noise is difficult to handle due to its unpredictability. Although there is a lot of research regarding the influence of noise on retrieval errors, few studies have focused on the mechanism. In this study, we selected the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) algorithm—the representative of the iterative spectral smoothness temperature-emissivity separation algorithm family—as the research object and proposed an improved algorithm. First, we analyzed the influence mechanism of noise on the retrieval errors of ARTEMISS in theory. Second, we carried out a simulation and inversion experiment and analyzed the relationship between instrument spectral resolution, noise level, the ARTEMISS parameter setting and the retrieval errors separately. Last, we proposed an improved method (resolution-degrade-based spectral smoothness algorithm, RDSS) based on the mechanism and law of the influence of noise on retrieval errors and provided corresponding suggestions on instrument design. The results show that RDSS improves the accuracy of temperature inversion and is more effective for thermal infrared data with a high noise level and high spectral resolution, which can reduce the LST inversion error by up to 0.75 K and the LSE median absolute deviation (MAD) by 31%. In the presence of noise in HTIR data, the RDSS algorithm performs better than the ARTEMISS algorithm in terms of temperature-emissivity separation.

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