Development of a combined bidirectional reflectance and directional emittance model for polarization modeling

Conant, John A.; Iannarilli, Frank J.

doi:10.1117/12.452890

Cited by 6 publications

(2 citation statements)

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“…This is what one may expect from the PCA theory. The "all-PC" result shown in Figure 5b is obtained by using Equations (18) and (19) with all PCs included. It has the smallest RMS error as expected.…”

Section: Methodology Of Hsbr Algorithmmentioning

confidence: 99%

“…The land surface BRDFs have been investigated for decades in both laboratory and field environments. Many empirical, semi-empirical, and physical BRDF models have been developed for homogeneous materials [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. However, the reflective spectrum of a material obtained in laboratory may not represent the BRDF of the same type of material in the field very well.…”

Section: Introductionmentioning

confidence: 99%

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A Hyperspectral Bidirectional Reflectance Model for Land Surface

Yang

Liu

2020

Sensors

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A hyperspectral bidirectional reflectance (HSBR) model for land surface has been developed in this work. The HSBR model includes a very diverse land surface bidirectional reflectance distribution function (BRDF) database with ~40,000 spectra. The BRDF database is saved as Ross-Li parameters, which can generate hyperspectral reflectance spectra at different sensor and solar observation geometries. The HSBR model also provides an improved method for generating hyperspectral surface reflectance using multiband satellite measurements. It is shown that the land surface reflective spectrum can be easily simulated using BRDF parameters or reflectance at few preselected wavelengths. The HSBR model is validated using the U.S. Geological Survey (USGS) vegetation database and the AVIRIS reflectance product. The simulated reflective spectra fit the measurements very well with standard deviations normally smaller than 0.01 in the unit of reflectivity. The HSBR model could be used to significantly improve the quality of the reflectance products of satellite and airborne sensors. It also plays important role for intercalibration among space-based instruments and other land surface related applications.

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Section: Methodology Of Hsbr Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Bidirectional Reflectance Model for Land Surface

Yang

Liu

2020

Sensors

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Quantitative camouflage paint selection for the CH‐47F helicopter

Bacon

Iannarilli

Conant

et al. 2009

Color Research & Application

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We have determined optimal minimum-conspicuity monocoat paint colors for the CH-47F Chinook helicopter, viewed photopically against forest, desert, and sky backgrounds. Our methodology combines use of a validated spectroradiometric model for rigorous 3D signature prediction with statistics of varying background fields and a CIE color difference metric. The study considered a large subset of the Federal Standard 595 (FS595) paint inventory. Each paint color was rigorously modeled with bidirectional reflectance distribution function scattering properties to match existing army paint and spectral reflectances to match spectrophotometer measurements of FS595 reference samples. We devised and validated a method to impute statistical variation in background radiances over environmental conditions consistent with the aircraft radiometric computations. Using a visual jury, we informally calibrated the CIE 1994 color difference formula (which gauges both luminance and chromaticity contrast) to gauge how each paint performed against each background, for varying range, view direction, and sun location. The statistical dispersions in performance were summarized for the CH-47F Program Manager, who selected the best overall paint for the CH-47F fleet. We found paints that were optimized to a specific background (forest, desert, etc.) yielded enhanced performance against those backgrounds, as would be expected, and that those paints were better than the paint used on CH-47s in the current US inventory.

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