Hyperspectral camera as a compact payload architecture for remote sensing applications

David Morales-Norato; Sergio Urrea; Hans Garcia; Julian Rodriguez-Ferreira; Elizabeth Martinez; Henry Arguello; Alberto Silva-Lora; Alberto Silva-Lora; Rafael Torres; Rafael Torres; Ignacio F. Acero; Francisco L. Hernández; Lorena P. Cárdenas; Sonia Rincón

doi:10.1364/AO.476978

Applied Optics
Vol. 62,
Issue 8,
pp. C88-C98
(2023)
•https://doi.org/10.1364/AO.476978

Hyperspectral camera as a compact payload architecture for remote sensing applications

David Morales-Norato, Sergio Urrea, Hans Garcia, Julian Rodriguez-Ferreira, Elizabeth Martinez, Henry Arguello, Alberto Silva-Lora, Rafael Torres, Ignacio F. Acero, Francisco L. Hernández, Lorena P. Cárdenas, and Sonia Rincón

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David Morales-Norato, Sergio Urrea, Hans Garcia, Julian Rodriguez-Ferreira, Elizabeth Martinez, Henry Arguello, Alberto Silva-Lora, Rafael Torres, Ignacio F. Acero, Francisco L. Hernández, Lorena P. Cárdenas, and Sonia Rincón, "Hyperspectral camera as a compact payload architecture for remote sensing applications," Appl. Opt. 62, C88-C98 (2023)

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Table of Contents Category
- Optical Design and Fabrication
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The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: September 29, 2022
- Revised Manuscript: December 21, 2022
- Manuscript Accepted: January 10, 2023
- Published: February 10, 2023
Virtual Issues
Applied Optics Latin America Optics and Photonics 2022 (2022)

Abstract

Monitoring and observation over the surface of the Earth have been a matter of global interest. In this path, recent efforts aim to develop a spatial mission to perform remote sensing applications. Mainly, CubeSat nanosatellites have emerged as a standard for developing low-weight and small-sized instruments. In terms of payloads, state-of-the-art optical systems for CubeSats are expensive and designed to work in general use cases. To overcome these limitations, this paper presents a 1.4 U compact optical system to acquire spectral images from a CubeSat standard satellite at the height of 550 km. To validate the proposed architecture, optical simulations using ray tracing simulation software are presented. Because the performance of computer vision tasks is highly related to data quality, we compared the optical system in terms of the classification performance on a real remote sensing application. The performances of the optical characterization and land cover classification show that the proposed optical system achieves a compact instrument, operating at a spectral range from 450 nm to 900 nm discretized on 35 spectral bands. The optical system has an overall $f$-number of 3.41 with a ground sampling distance of 52.8 m and a swath of 40 km. Additionally, the design parameters for each optical element are publicly available for validation, repeatability, and reproducibility of the results.

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Data availability

The KSC dataset used in the experiments reported in this paper is available in [44].

44. M. Graña, M. A. Veganzons, and B. Ayerdi, “Hyperspectral remote sensing scenes,” Grupo de Inteligencia Computacional (GIC) (2022), https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

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Surface ( $M k$ )	Radius $(\frac{1}{O_{k}} m m)$	Conic Constant $(G_{k})$	Section
M1	−279.99992	−1.28553	Telescope
M2	−133.33333	−8.79462	Telescope
M3	−65.49155	−1.68698	Telescope
M4	83.95355	−0.99832	Spectrometer
M5	−80.03207	−0.89272	Spectrometer

	Satellite Spectral Cameras
	Commercial					Research
Parameters	TriScape100	Hyper Scape100	Chameleon Imager	Mantis Imager	Hyper Scout 2	NACHOS [26]	CSIMBA [27]	HYPSO-1 [28]	Proposed
Spatial resolution (GSD)	4.75 m	4.75 m	20/40 m	32 m	69 m	400 m	20 m	100 m	52.8 m
Swath	19.4 km	19.4 km	20 km	32 km	150 km	140 km	80 km	70 km	40 km
Spectral bands	3	32	148	148	45	350	85	215	35
Physical size ( ${cm}^{3}$ )	$9.8 \times 9.8 \times 17.6$	$9.8 \times 9.8 \times 17.6$	$10 \times 10 \times 20$	$10 \times 10 \times 6.5$	$10 \times 10 \times 20$	$10 \times 10 \times 20$	$20 \times 20 \times 30$	$6.5 \times 6.5 \times 20$	$10 \times 10 \times 13.95$
CubeSat units	2U	2U	2U	1U	2U	2U	12U	2U	2U
Spectral range	450–670 nm	450–900 nm	470–900 nm	470–900 nm	400–1000 nm	290–500 nm	475–900 nm	200–967 nm	450–900 nm
Open design	NO	NO	NO	NO	NO	NO	NO	YES	YES
Type	Snapshot	Push broom	Push broom	Push broom	Push broom	Push broom	Push broom	Push broom	Push Broom

	Decimation
Spectral Cameras	$d_{x}$	$d_{y}$	$d_{λ}$
Mantis	1	1	1
Proposed (I, P and C) and HyperScout 2	2	2	4

Surface ( $M k$ )	Radius $(\frac{1}{O_{k}} m m)$	Conic Constant $(G_{k})$	Section
M1	−279.99992	−1.28553	Telescope
M2	−133.33333	−8.79462	Telescope
M3	−65.49155	−1.68698	Telescope
M4	83.95355	−0.99832	Spectrometer
M5	−80.03207	−0.89272	Spectrometer

	Satellite Spectral Cameras
	Commercial					Research
Parameters	TriScape100	Hyper Scape100	Chameleon Imager	Mantis Imager	Hyper Scout 2	NACHOS [26]	CSIMBA [27]	HYPSO-1 [28]	Proposed
Spatial resolution (GSD)	4.75 m	4.75 m	20/40 m	32 m	69 m	400 m	20 m	100 m	52.8 m
Swath	19.4 km	19.4 km	20 km	32 km	150 km	140 km	80 km	70 km	40 km
Spectral bands	3	32	148	148	45	350	85	215	35
Physical size ( ${cm}^{3}$ )	$9.8 \times 9.8 \times 17.6$	$9.8 \times 9.8 \times 17.6$	$10 \times 10 \times 20$	$10 \times 10 \times 6.5$	$10 \times 10 \times 20$	$10 \times 10 \times 20$	$20 \times 20 \times 30$	$6.5 \times 6.5 \times 20$	$10 \times 10 \times 13.95$
CubeSat units	2U	2U	2U	1U	2U	2U	12U	2U	2U
Spectral range	450–670 nm	450–900 nm	470–900 nm	470–900 nm	400–1000 nm	290–500 nm	475–900 nm	200–967 nm	450–900 nm
Open design	NO	NO	NO	NO	NO	NO	NO	YES	YES
Type	Snapshot	Push broom	Push broom	Push broom	Push broom	Push broom	Push broom	Push broom	Push Broom

Abstract

Data availability

Cited By

Figures (13)

Tables (3)

Equations (8)

Applied Optics