Radiometric spectral fusion of VNIR and SWIR hyperspectral cameras

Grillini, Federico; Thomas, Jean‐Baptiste; George, Sony

doi:10.2352/issn.2169-2629.2021.29.276

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Their approach involved optimizing the difference in the overlap region of the two sensors (900-1000 nm), aiming to identify coefficients that match the radiance spectra of different Spectralon panels, ensuring alignment with their corresponding reflectance spectra. 11 Despite these contributions, a notable gap remains in establishing a robust workflow for both lab-based and UAS-based remote sensing data fusion, emphasizing the need for further exploration, particularly in the near-infrared (VNIR) and short-wave infrared (SWIR) regions.…”

Section: Introductionmentioning

confidence: 99%

Preserving legacies, pioneering frontiers: multi-sensor image fusion, from medieval manuscripts to UAS-based sensing

Hassanzadeh,

Macalintal,

Messinger

2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

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Data fusion, which involves integrating data from multiple sources, is increasingly valuable across various fields due to its ability to enhance information quality, accuracy, and reliability. This process enables a more comprehensive understanding of complex phenomena by merging diverse datasets, providing insights that are otherwise unattainable. In the realm of remote sensing, where precise data acquisition is critical, fusion techniques have become indispensable, benefiting applications such as object detection, classification, and change detection. While much emphasis has been placed on spatial sharpening techniques in published studies, there remains a notable gap in establishing robust workflows for both lab-based and UAS-based remote sensing data fusion, particularly in the near-infrared (VNIR) and short-wave infrared (SWIR) regions. This study aims to investigate VNIR-SWIR fusion using data sourced from a medieval manuscript in a controlled laboratory environment and from UASbased sensors in a real-world setting, addressing differences in system parameters and processing workflows. Despite challenges such as image registration issues, our analysis has yielded promising results, underscoring the importance of ongoing refinement in fusion methodologies to ensure comprehensive data interpretation and analysis across diverse datasets and environments.

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Section: Introductionmentioning

confidence: 99%

Preserving legacies, pioneering frontiers: multi-sensor image fusion, from medieval manuscripts to UAS-based sensing

Hassanzadeh,

Macalintal,

Messinger

2024

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

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Trace metal content prediction along an AMD (acid mine drainage)-contaminated stream draining a coal mine using VNIR–SWIR spectroscopy

Abrahams,

Carranza

2023

Environ Monit Assess

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The current study investigated the use of VNIR–SWIR (visible/near infrared to short-wavelength infrared: 400–2500 nm) spectroscopy for predicting trace metals in overbank sediments collected in the study site. Here, we (i) derived spectral absorption feature parameters (SAFPs) from measured ground spectra for correlation with trace metal (Pb, Cd, As, and Cu) contents in overbank sediments, (ii) built univariate regression models to predict trace metal concentrations using the SAFPs, and (iii) evaluated the predictive capacities of the regression models. The derived SAFPs associated with goethite in overbank sediments were Depth433b, Asym433b, and Width433b, and those associated with kaolinite in overbank sediments were Depth1366b, Asym1366b, Width1366b, Depth2208b, Asym2208b, and Width2208b. Cadmium in the overbank sediments showed the strongest correlations with the goethite-related SAFPs, whereas Pb, As, and Cu showed strong correlations with goethite- and kaolinite-related SAFPs. The best predictive models were obtained for Cu (R2 = 0.73, SEE = 0.15) and Pb (R2 = 0.73, SEE = 0.21), while weaker models were obtained for As (R2 = 0.46, SEE = 0.31) and Cd (R2 = 0.17, SEE = 0.81). The results suggest that trace metals can be predicted indirectly using the SAFPs associated with goethite and kaolinite. This is an important benefit of VNIR–SWIR spectroscopy considering the difficulty in analyzing “trace” metal concentrations, on large scales, using conventional geochemical methods.

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Logistic splicing correction for VNIR–SWIR reflectance imaging spectroscopy

Grillini¹,

Thomas²,

George³

2023

Opt. Lett.

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In the field of spectroscopy, a splicing correction is a process by which two spectra captured with different sensors in adjacent or overlapping electromagnetic spectrum ranges are smoothly connected. In our study, we extend this concept to the case of reflectance imaging spectroscopy in the visible–near-infrared (VNIR) and short-wave infrared (SWIR), accounting for additional sources of noise that arise at the pixel level. The proposed approach exploits the adaptive fitting of a logistic function to compute correcting coefficients that harmonize the two spectral sets. This short Letter addresses usage conditions and compares results against the existing state of the art.

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Radiometric spectral fusion of VNIR and SWIR hyperspectral cameras

Cited by 3 publications

References 9 publications

Preserving legacies, pioneering frontiers: multi-sensor image fusion, from medieval manuscripts to UAS-based sensing

Preserving legacies, pioneering frontiers: multi-sensor image fusion, from medieval manuscripts to UAS-based sensing

Trace metal content prediction along an AMD (acid mine drainage)-contaminated stream draining a coal mine using VNIR–SWIR spectroscopy

Logistic splicing correction for VNIR–SWIR reflectance imaging spectroscopy

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