Full characterization of spatial coregistration errors and spatial resolution in spectral imagers

Torkildsen, Hans Erling; Skauli, T.

doi:10.1364/ol.43.003814

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…While this is an idealization, real imaging systems show that this is hardly the case. 50,51 Again, we posit that our results present the best-case scenario in terms of algorithms' fusion ability as performance will likely be reduced when variations in PSF parameters are included in simulating LRHSI bands.…”

Section: Resultsmentioning

confidence: 81%

Image fusion of hyperspectral and multispectral imagery using nearest-neighbor diffusion

Ducay

Messinger

2023

J. Appl. Rem. Sens.

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The spatial resolution of a hyperspectral image (HSI) can be enhanced by utilizing a co-registered high-resolution multispectral image (MSI) via an image fusion algorithm. This is an active area of research with applications ranging from precision agriculture, to security issues, to mineral identification and mapping, etc. The nearest-neighbor diffusion (NNDiffuse) algorithm has seen tremendous success in being utilized as a pansharpening algorithm to fuse an MSI with a broadband high-resolution panchromatic image and is implemented in commercial software applications, such as ENVI (L3Harris). We extended NNDiffuse to the problem of hyprespectral-multispectral image fusion (HS + MS). Hyperspectral pansharpening is a special case of this: a single high-resolution broadband (panchromatic) image is fused with a lowresolution HSI. Deep-learning (DL)-based methods can achieve excellent results in the area of HS + MS data fusion, but DL algorithms are data hungry: they need extensive training data, require special computing architectures, are slower to implement, etc. NNDiffuse introduces much less spectral distortions compared to state-of-the-art methods, does not need any training data or expensive computing resources, and is significantly faster compared to DL-based approaches. We: (1) demonstrate the utility of NNDiffuse in low-resolution HSI-MSI sharpening; (2) test three deep learning-based image fusion algorithms and compare performance with NNDiffuse and other non-DL-based fusion algorithms using global/image-wide quality metrics; and (3) assess fusion performance using an application based approach: adaptive coherence estimator target detection. The proposed method is tested against several datasets of varying scene content and complexity, and we demonstrate that the NNDiffuse fusion method outperforms the other baseline methods when the application of target detection is considered.

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Section: Resultsmentioning

confidence: 81%

Image fusion of hyperspectral and multispectral imagery using nearest-neighbor diffusion

Ducay

Messinger

2023

J. Appl. Rem. Sens.

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“…Coregistration errors should also be considered in the analyses. 8 It is difficult to give a general estimate of this error since it depends on both the relative motion between the sensor and the target and on the specific sensor system realization. For a situation with translational motion only, the registration error will be very small.…”

Section: Discussionmentioning

confidence: 99%

High spatial resolution hyperspectral camera based on exponentially variable filter

Renhorn¹,

Axelsson

2019

Opt. Eng.

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The architecture and calibration of a hyperspectral imaging sensor based on an exponentially continuously variable narrow-band transmission filter is described. The system design allows for great flexibility in choice of sensors and lenses to be used. Spectral and radiometric calibration using lenses of different focal length and vignetting characteristics is described. The point-spread-function at different wavelengths depends on the lens design and the f-number. The advantage of using a tilt/shift lens is demonstrated. Low f-number lenses show vignetting, which influences both the spectral and radiometric calibration. Retroeffects in the microlenses of the focal plane array are observed but to a large extent will be remedied by future improvements in the optical filter. Noise properties of the sensor system are discussed, and signal-to-noise ratios estimated. From the model, it is possible to obtain parametric performance variations based on the properties of key components. Finally, the sensor performance is indicated by demonstrating a spectral image.

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“…The difference in extinction coefficient found when measuring each dye in gelatin compared with aqueous solution is not fully understood and requires further study. These phantoms are also spatially uniform meaning that, whilst these could be used to determine the performance of these models spatially using hyperspectral imaging systems, other methods would be required to assess spatial resolution of these systems [34,35]. Second, the shift in absorbance of the third chromophore made it difficult to model thereby limiting the investigation of the impact of additional chromophores.…”

Section: Modelmentioning

confidence: 99%

A comparative study of analytical models of diffuse reflectance in homogeneous biological tissues: Gelatin‐based phantoms and Monte Carlo experiments

Bahl,

Segaud,

Xie

et al. 2024

Journal of Biophotonics

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Information about tissue oxygen saturation (StO2) and other related important physiological parameters can be extracted from diffuse reflectance spectra measured through non‐contact imaging. Three analytical optical reflectance models for homogeneous, semi‐infinite, tissue have been proposed (Modified Beer–Lambert, Jacques 1999, Yudovsky 2009) but these have not been directly compared for tissue parameter extraction purposes. We compare these analytical models using Monte Carlo (MC) simulated diffuse reflectance spectra and controlled gelatin‐based phantoms with measured diffuse reflectance spectra and known ground truth composition parameters. The Yudovsky model performed best against MC simulations and measured spectra of tissue phantoms in terms of goodness of fit and parameter extraction accuracy followed closely by Jacques' model. In this study, Yudovsky's model appeared most robust; however, our results demonstrated that both Yudovsky and Jacques models are suitable for modeling tissue that can be approximated as a single, homogeneous, semi‐infinite slab.

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Full characterization of spatial coregistration errors and spatial resolution in spectral imagers

Cited by 10 publications

References 8 publications

Image fusion of hyperspectral and multispectral imagery using nearest-neighbor diffusion

Image fusion of hyperspectral and multispectral imagery using nearest-neighbor diffusion

High spatial resolution hyperspectral camera based on exponentially variable filter

A comparative study of analytical models of diffuse reflectance in homogeneous biological tissues: Gelatin‐based phantoms and Monte Carlo experiments

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