Design of non-Gaussian multispectral shortwave infrared filters assessed by surface spectral reflectances on the ECOSTRESS library

Germano S. Fonseca; Germano S. Fonseca; Leonardo B. de Sá; José Gabriel R. C. Gomes

doi:10.1364/JOSAA.480571

Journal of the Optical Society of America A
Vol. 40,
Issue 5,
pp. 1006-1015
(2023)
•https://doi.org/10.1364/JOSAA.480571

Design of non-Gaussian multispectral shortwave infrared filters assessed by surface spectral reflectances on the ECOSTRESS library

Germano S. Fonseca, Leonardo B. de Sá, and José Gabriel R. C. Gomes

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Germano S. Fonseca, Leonardo B. de Sá, and José Gabriel R. C. Gomes, "Design of non-Gaussian multispectral shortwave infrared filters assessed by surface spectral reflectances on the ECOSTRESS library," J. Opt. Soc. Am. A 40, 1006-1015 (2023)

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Related Topics
Table of Contents Category
- Color, Spectroscopy, and Spectral Imaging
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About this Article
History
- Original Manuscript: November 9, 2022
- Revised Manuscript: March 25, 2023
- Manuscript Accepted: March 29, 2023
- Published: May 1, 2023
Virtual Issues
JOSA B Joint Feature Issue with Journal of the Optical Society of America A and
Journal of the Optical Society of America B: OPTICS IN SOUTH AMERICA (2023)

JOSA A Joint Feature Issue with Journal of the Optical Society of America A and
Journal of the Optical Society of America B: OPTICS IN SOUTH AMERICA (2023)

Abstract

This paper addresses the multispectral filter design problem for spectral ranges where a viewing subspace is not defined. The methodology of color filter design is extended to this case, which allows the optimization of custom filter transmittance that meets the physical constraints of available fabrication methods. Multispectral shortwave infrared filters are then designed for two scenarios: spectral reconstruction and false-color representation. The Monte Carlo method is used to verify the filter performance degradation due to deviations in fabrication. The results obtained indicate that the proposed method is useful for designing multispectral filters to be fabricated using generic processes without any additional constraints.

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Supplementary Material (2)

Name	Description
Code 1	Python code to design a non-Gaussian SWIR multispectral filter. This code was used to obtain the results of the main paper. More information is in README.md.
Supplement 1	Supplemental document with additional figures related to Sections 4.C and 4.D.

Data availability

Data underlying the results presented in this paper are available in [49,60]. In [61], some remarks about the SWIR subset of the ECOSTRESS library are made. The SWIR subset and the illuminant used in this paper are also available in Code 1, Ref. [64].

49. American Society for Testing and Materials Committee G03 on Weathering and Durability, “Standard tables for reference solar spectral irradiances: direct normal and hemispherical on 37° tilted surface,” Technical report ASTM G173 (ASTM International, 2003).

60. S. K. Meerdink, S. J. Hook, D. A. Roberts, and E. A. Abbott, “The ECOSTRESS spectral library version 1.0,” Remote Sens. Environ. 230, 111196 (2019). [CrossRef]

61. G. S. Fonseca, A. C. G. dos Santos, L. B. de Sá, and J. G. R. C. Gomes, “Linear models for SWIR surface spectra from the ECOSTRESS library,” Proc. SPIE 11727, 117271J (2021). [CrossRef]

64. G. Fonseca, “Python code to design non-Gaussian SWIR multispectral filter,” figshare (2023), https://doi.org/10.6084/m9.figshare.22327882.

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Equations (9)

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Filter Set	$ν$	$e_{a v e} / 10^{- 6}$	$e_{m a x} / 10^{- 3}$
5 equispaced	0.8296	369.2	12.3
6 equispaced	0.8890	267.3	8.0
7 equispaced	0.9427	159.7	5.1
8 equispaced	0.9468	115.4	3.4
9 equispaced	0.9525	22.3	0.8

Filter Set	$ν$	$e_{a v e} / 10^{- 6}$	$e_{m a x} / 10^{- 3}$
From [40]	0.7343	$672 \times 10^{3}$	$32.2 \times 10^{3}$
3 equispaced	0.6567	$2.9 \times 10^{6}$	$64.4 \times 10^{3}$
4 equispaced	0.6868	$986 \times 10^{3}$	$38.6 \times 10^{3}$
5 equispaced	0.8929	$98 \times 10^{3}$	$1.9 \times 10^{3}$
6 equispaced	0.9149	104.4	3.2
7 equispaced	0.9655	43.9	1.4
8 equispaced	0.9502	49.9	1.4
9 equispaced	0.9607	8.5	$291.4 \times 10^{- 3}$

Filter Set	Model	$ν$	$e_{a v e} / 10^{- 6}$	$e_{m a x} / 10^{- 3}$
1	Single Gaussian	0.9387	57.5	2.2
2	Double Gaussian	0.9865	24.7	1.3

Filter Set	Model	$ν$	$e_{a v e} / 10^{- 6}$	$e_{m a x} / 10^{- 3}$
3	Single Gaussian	0.9208	63.7	1.5
4	Double Gaussian	0.9816	46.5	1.8
5	Triple Gaussian	0.9933	7.214	0.2

Filter Set	$ν$	$e_{a v e} / 10^{- 6}$	$e_{m a x} / 10^{- 3}$
5 equispaced	0.8296	369.2	12.3
6 equispaced	0.8890	267.3	8.0
7 equispaced	0.9427	159.7	5.1
8 equispaced	0.9468	115.4	3.4
9 equispaced	0.9525	22.3	0.8

Abstract

Supplementary Material (2)

Data availability

Cited By

Figures (6)

Tables (4)

Equations (9)

Journal of the Optical Society of America A