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Absorption-Feature-Guided Distance-Decoupled Estimation and Band Selection for LWIR Hyperspectral Passive Ranging

T0 review · 2 major / 1 minor · reviewed 2026-07-01 · grok-4.3

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Pith's one-line read The ADER framework recovers LiDAR-consistent distances from LWIR hyperspectral data by decoupling emissivity via B-spline smoothness and ozone absorption classification.

desk verdict ADER gives a concrete speed-up for LWIR passive ranging via B-splines and ozone cues, but the validation stays mostly qualitative. read the letter →

arxiv 2606.31824 v1 pith:4H7Z2TYO submitted 2026-06-30 cs.CV

Shuo Liu , Chen Fan , Zhihe Chen , Xiaolin Huang , Lilian Zhang This is my paper · ORCID
classification cs.CV
keywords LWIRhyperspectralpassiverangingatmosphericabsorptionemissivityestimationbandselectionB-splinesmoothnessozonedistancedecoupling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to solve the ill-posed inversion of distance from LWIR hyperspectral radiance, where atmospheric absorption signatures are entangled with target temperature, emissivity, and path radiance. The central approach is the ADER framework, which represents emissivity through B-spline control points under a smoothness prior to enable separate estimation of distance. Pixels are first classified into emission-dominant and reflection-dominant groups using ozone absorption cues, after which tailored compensation and one-dimensional residual minimization recover distance for each type. A greedy band selection step based on multi-scene Fisher information further reduces the number of required spectral channels. If the method works as described, passive ranging becomes feasible at scale in natural scenes without active sensors and with far less computation than full-band joint optimization.

What carries the argument

Absorption-Guided Distance-Decoupled Estimation and Refinement (ADER) framework, which separates distance from emissivity by B-spline smoothness prior and ozone-based pixel classification followed by type-specific compensation.

What would settle it

A comparison showing that ADER distance maps deviate substantially from independent LiDAR ground truth in scenes where emissivity contains sharp spatial variations that violate the B-spline smoothness prior.

Watch

Extended reading notes

Core claim

ADER represents emissivity with B-spline control points under a smoothness prior that suppresses overfitting to atmospheric absorption, classifies pixels into emission-dominant and reflection-dominant groups using ozone-absorption cues, compensates path radiance and transmittance to estimate distance by one-dimensional absorption-residual minimization for emission-dominant pixels, refines the estimate with downwelling-radiance compensation for reflection-dominant pixels, and applies greedy band selection based on multi-scene effective Fisher information; experiments on real scenes recover LiDAR-consistent spatial distance structures under both full-band and 20-band settings while improving a

Load-bearing premise

The B-spline smoothness prior on emissivity sufficiently suppresses overfitting to atmospheric absorption structures while still allowing accurate distance recovery.

Editorial extensions

If this is right

  • Recovers LiDAR-consistent spatial distance structures in real scenes.
  • Maintains performance under both full-band and 20-band settings.
  • Improves ranging accuracy in the evaluated regions.
  • Achieves approximately two orders of magnitude speedup over public full-band hyperspectral ranging methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The pixel classification and compensation strategy could extend to other wavelength ranges that exhibit distinct atmospheric absorption lines.
  • Fisher-information band selection might be repurposed to optimize spectral channels for estimating parameters other than distance, such as surface temperature.
  • The decoupling of emissivity via smoothness priors may lower computational cost in broader hyperspectral unmixing or material identification tasks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces the Absorption-Feature-Guided Distance-Decoupled Estimation and Refinement (ADER) framework for LWIR hyperspectral passive ranging. It represents emissivity via B-splines under a smoothness prior to suppress overfitting to atmospheric absorption, classifies pixels into emission- and reflection-dominant groups using ozone cues, applies path-radiance/transmittance compensation followed by 1-D absorption-residual minimization for emission-dominant pixels and downwelling compensation for reflection-dominant pixels, and performs greedy band selection via multi-scene effective Fisher information. Real-scene experiments are reported to recover LiDAR-consistent distance maps under full-band and 20-band settings, improve accuracy in evaluated regions, and yield ~100x speedup versus a public full-band method.

Significance. If the central performance claims hold under rigorous validation, the work would be significant for remote-sensing applications by providing a computationally tractable solution to the ill-posed distance inversion problem that explicitly exploits sharp atmospheric absorption features while reducing spectral redundancy. The combination of smoothness-constrained B-spline emissivity modeling and pixel-type classification addresses a key practical bottleneck in existing full-band joint-optimization approaches.

major comments (2)
  1. [Experiments] Experiments section: the central claim of LiDAR-consistent distance recovery and improved ranging accuracy is presented without quantitative error statistics (RMSE, MAE, or correlation with LiDAR ground truth), without description of the spatial regions used for evaluation, without error bars or cross-validation, and without discussion of failure cases. This directly limits assessment of whether the reported spatial structures constitute a substantive improvement.
  2. [ADER framework] ADER framework description (B-spline smoothness prior): the distance estimate for emission-dominant pixels is obtained via 1-D absorption-residual minimization after compensation; the claim that the B-spline smoothness prior sufficiently decouples emissivity from atmospheric absorption lines (ozone and others) is load-bearing, yet no ablation on the smoothness weight, no recovered emissivity spectra versus laboratory references, and no sensitivity analysis are supplied to confirm the prior operates in the regime that separates the signals rather than trading one bias for another.
minor comments (1)
  1. [Abstract] Abstract: the reported 'approximately two orders of magnitude speedup' does not identify the exact baseline implementation or hardware platform, reducing reproducibility.

Simulated Author's Rebuttal

2 responses · 1 unresolved

Thank you for the constructive feedback. We address the major comments below and will revise the manuscript to strengthen the evaluation and analysis where feasible.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: the central claim of LiDAR-consistent distance recovery and improved ranging accuracy is presented without quantitative error statistics (RMSE, MAE, or correlation with LiDAR ground truth), without description of the spatial regions used for evaluation, without error bars or cross-validation, and without discussion of failure cases. This directly limits assessment of whether the reported spatial structures constitute a substantive improvement.

    Authors: We agree that quantitative validation metrics are needed to substantiate the claims. In the revised manuscript we will report RMSE, MAE, and correlation coefficients against LiDAR ground truth for the evaluated regions, explicitly describe the spatial regions (e.g., building facades, vegetation, and road surfaces shown in the figures), include error bars or standard deviations across scenes, and discuss observed failure cases such as low-signal or strongly reflective pixels. These additions will be computed from the existing LiDAR-aligned data. revision: yes

  2. Referee: [ADER framework] ADER framework description (B-spline smoothness prior): the distance estimate for emission-dominant pixels is obtained via 1-D absorption-residual minimization after compensation; the claim that the B-spline smoothness prior sufficiently decouples emissivity from atmospheric absorption lines (ozone and others) is load-bearing, yet no ablation on the smoothness weight, no recovered emissivity spectra versus laboratory references, and no sensitivity analysis are supplied to confirm the prior operates in the regime that separates the signals rather than trading one bias for another.

    Authors: We will add an ablation study on the smoothness weight λ and a sensitivity analysis showing its impact on distance estimates across scenes. We will also include representative recovered emissivity spectra from the real scenes to illustrate the effect of the prior. Laboratory reference emissivity spectra for the precise materials present in the outdoor scenes are unavailable, so direct comparisons cannot be provided. revision: partial

standing simulated objections not resolved
  • Direct comparison of recovered emissivity spectra against laboratory references, because laboratory emissivity data for the specific real-scene materials are not available.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detected in derivation chain

full rationale

The ADER framework is constructed from the standard radiative-transfer equation by adding an explicit B-spline emissivity representation under a smoothness prior, ozone-based pixel classification, and separate 1-D residual minimization for emission-dominant pixels plus downwelling compensation for reflection-dominant pixels. Distance recovery is obtained by direct optimization after path compensation rather than by re-using any fitted parameter or self-defined quantity from the same data; band selection uses multi-scene Fisher information on the distance parameter. No self-citations appear as load-bearing steps, no ansatz is smuggled via prior work, and no prediction reduces to its own input by construction. The derivation therefore remains independent of the target outputs.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are enumerated beyond the standard radiative-transfer model and the B-spline smoothness prior.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Absorption-Feature-Guided Distance-Decoupled Estimation and Band Selection for LWIR Hyperspectral Passive Ranging." pith.science (2026). https://pith.science/paper/4H7Z2TYO

@misc{pith2026260631824,
  author       = {Pith},
  title        = {Pith review of: Absorption-Feature-Guided Distance-Decoupled Estimation and Band Selection for LWIR Hyperspectral Passive Ranging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4H7Z2TYO}},
  note         = {Machine review of arXiv:2606.31824}
}
read the original abstract

Long-wave infrared (LWIR) hyperspectral observations contain distance-dependent atmospheric absorption signatures, providing a physical basis for long-range passive ranging. However, in natural scenes, these signatures are nonlinearly coupled with target temperature, material emissivity, and path radiance, making distance inversion from observed radiance ill posed. Existing methods typically rely on full-band measurements and pixel-wise joint optimization, which is computationally expensive and does not explicitly exploit sharp atmospheric absorption structures. This paper proposes an Absorption-Guided Distance-Decoupled Estimation and Refinement (ADER) framework for LWIR hyperspectral passive ranging. ADER represents emissivity with B-spline control points under a smoothness prior, suppressing overfitting to atmospheric absorption structures and enabling distance-decoupled estimation. It further uses ozone-absorption cues to classify pixels into emission-dominant and reflection-dominant groups. For emission-dominant pixels, ADER compensates path radiance and transmittance and estimates distance by one-dimensional absorption-residual minimization. For reflection-dominant pixels, ADER refines the initial estimate using downwelling-radiance compensation based on the complete radiative model. To reduce spectral redundancy, ADER also introduces a greedy band selection strategy based on multi-scene effective Fisher information for the distance parameter. Experiments on real scenes show that ADER recovers LiDAR-consistent spatial distance structures under both full-band and 20-band settings, improves ranging accuracy in the evaluated regions, and achieves approximately two orders of magnitude speedup over a public full-band hyperspectral ranging method.

Figures

Figures reproduced from arXiv: 2606.31824 by the authors.

Figure 1
Figure 1. Motivation, pipeline, and representative ranging results of the proposed ADER framework. The left part shows [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Schematic illustration of the radiative transfer model used in ADER. The observed radiance is decomposed into [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Comparison of B-spline fitting for material emissivity [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Pixel classification result based on downwelling [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Radiative simulation analysis using limestone emis [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Illustration of the distance-decoupling mechanism [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Fisher-information-based band selection analysis. (a) [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Real-scene ranging comparison. (a) ADER full-band. (b) ADER 20-band. (c) Approximately registered LiDAR map [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Reflection-compensated refinement on reflective boards. (a) Decoupled-only result, where the reflective boards inside [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Reflective-board ranging and histogram compari [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Profile comparison between ADER full-band and [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Cross-scene ranging validation on additional IH dataset scenes. The rows show the visible reference image, [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]

Discussion (0). Continue with ORCID to comment.

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Reference graph

Works this paper leans on

53 extracted references · 53 canonical work pages · cited by 1 Pith paper

  1. [1]

    Advances in single-photon lidar for autonomous vehicles: Work- ing principles, challenges, and recent advances,

    J. Rapp, J. Tachella, Y. Altmann, S. McLaughlin, and V . K. Goyal, “Advances in single-photon lidar for autonomous vehicles: Work- ing principles, challenges, and recent advances,”IEEE Signal Pro- cessing Magazine, vol. 37, no. 4, pp. 62–71, 2020

    work page 2020

  2. [2]

    Lidar measurement of snow depth: A review,

    J. S. Deems, T. H. Painter, and D. C. Finnegan, “Lidar measurement of snow depth: A review,”Journal of Glaciology, vol. 59, no. 215, pp. 467–479, 2013

    work page 2013

  3. [3]

    Evaluation of HoloLens tracking and depth sensing for indoor mapping applications,

    P . H ¨ubner, K. Clintworth, Q. Liu, M. Weinmann, and S. Wursthorn, “Evaluation of HoloLens tracking and depth sensing for indoor mapping applications,”Sensors, vol. 20, no. 4, p. 1021, 2020. ABSORPTION-FEATURE-GUIDED DISTANCE-DECOUPLED ESTIMATION AND BAND SELECTION FOR LWIR HYPERSPECTRAL PASSIVE RANGING 17

    work page 2020

  4. [4]

    An overview of lidar imaging systems for autonomous vehicles,

    S. Royo and M. Ballesta-Garcia, “An overview of lidar imaging systems for autonomous vehicles,”Applied Sciences, vol. 9, no. 19, p. 4093, 2019

    work page 2019

  5. [5]

    Stereo visual SLAM for autonomous vehicles: A review,

    B. Gao, H. Lang, and J. Ren, “Stereo visual SLAM for autonomous vehicles: A review,” in2020 IEEE International Conference on Sys- tems, Man, and Cybernetics (SMC), 2020, pp. 1316–1322

    work page 2020

  6. [6]

    Digging into self-supervised monocular depth estimation,

    C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, “Digging into self-supervised monocular depth estimation,” inProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3827–3837

    work page 2019

  7. [7]

    Regu- larizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark,

    K. Wang, Z. Zhang, Z. Yan, X. Li, B. Xu, J. Li, and J. Yang, “Regu- larizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark,” inProceedings of the IEEE/CVF In- ternational Conference on Computer Vision (ICCV), 2021, pp. 16 055– 16 064

    work page 2021

  8. [8]

    FoggyStereo: Stereo matching with fog vol- ume representation,

    C. Yao and L. Yu, “FoggyStereo: Stereo matching with fog vol- ume representation,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 13 043– 13 052

    work page 2022

Show all 53 references
  1. [9]

    Robust monocular depth estimation under challenging condi- tions,

    S. Gasperini, N. Morbitzer, H. Jung, N. Navab, and F. Tombari, “Robust monocular depth estimation under challenging condi- tions,” inProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 8177–8186

    2023

  2. [10]

    Heat-assisted detection and ranging,

    F. Bao, X. Wang, S. H. Sureshbabu, G. Sreekumar, L. Yang, V . Ag- garwal, V . N. Boddeti, and Z. Jacob, “Heat-assisted detection and ranging,”Nature, vol. 619, pp. 743–748, 2023

    2023

  3. [11]

    Shape from thermal radiation: Passive ranging using multi- spectral LWIR measurements,

    Y. Nagase, T. Kushida, K. Tanaka, T. Funatomi, and Y. Mukaigawa, “Shape from thermal radiation: Passive ranging using multi- spectral LWIR measurements,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 12 661–12 671

    2022

  4. [12]

    Absorption-based, passive range imaging from hyper- spectral thermal measurements,

    U. Dorken Gallastegi, H. Rueda-Chac ´on, M. J. Stevens, and V . K. Goyal, “Absorption-based, passive range imaging from hyper- spectral thermal measurements,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 5, pp. 4044–4060, 2025

    2025

  5. [13]

    Longwave infrared hyperspectral imaging: Principles, progress, and challenges,

    D. Manolakis, M. Pieper, E. Truslow, R. Lockwood, A. Weisner, J. Jacobson, and T. Cooley, “Longwave infrared hyperspectral imaging: Principles, progress, and challenges,”IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 2, pp. 72–100, 2019

    2019

  6. [14]

    D. G. Manolakis, R. B. Lockwood, and T. W. Cooley,Hyperspectral Imaging Remote Sensing: Physics, Sensors, and Algorithms. Cam- bridge University Press, 2016

    2016

  7. [15]

    M. T. Eismann,Hyperspectral Remote Sensing. SPIE Press, 2012

    2012

  8. [16]

    Absorption-based hyperspectral thermal ranging: Perfor- mance analyses, optimization, and simulations,

    U. Dorken Gallastegi, H. Rueda-Chac ´on, M. J. Stevens, and V . K. Goyal, “Absorption-based hyperspectral thermal ranging: Perfor- mance analyses, optimization, and simulations,”Optics Express, vol. 32, no. 1, pp. 151–166, 2024

    2024

  9. [17]

    An in-scene method for atmospheric compensation of thermal hyperspectral data,

    S. J. Young, B. R. Johnson, and J. A. Hackwell, “An in-scene method for atmospheric compensation of thermal hyperspectral data,”Journal of Geophysical Research: Atmospheres, vol. 107, no. D24, p. 4774, 2002

    2002

  10. [18]

    AT2ES: Simultaneous atmospheric transmittance-temperature-emissivity separation using online up- per midwave infrared hyperspectral images,

    S. Kim, J. Shin, and S. Kim, “AT2ES: Simultaneous atmospheric transmittance-temperature-emissivity separation using online up- per midwave infrared hyperspectral images,”Remote Sensing, vol. 13, no. 7, p. 1249, 2021

    2021

  11. [19]

    Depth sensing by near-infrared light absorption in water,

    Y. Asano, Y. Zheng, K. Nishino, and I. Sato, “Depth sensing by near-infrared light absorption in water,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 8, pp. 2611– 2622, 2021

    2021

  12. [20]

    Affine transform representation for reducing calibration cost on absorption-based LWIR depth sensing,

    T. Kushida, R. Nakamura, H. Matsuda, W. Chen, and K. Tanaka, “Affine transform representation for reducing calibration cost on absorption-based LWIR depth sensing,”Scientific Reports, vol. 14, no. 1, p. 26429, 2024

    2024

  13. [21]

    Ozone cues mit- igate reflected downwelling radiance in LWIR absorption-based ranging,

    U. Dorken Gallastegi, W. Shangguan, V . Choudhary, A. Agarwal, H. Rueda-Chac´on, M. J. Stevens, and V . K. Goyal, “Ozone cues mit- igate reflected downwelling radiance in LWIR absorption-based ranging,”IEEE Transactions on Computational Imaging, vol. 12, pp. 587–600, 2026

    2026

  14. [22]

    Hyperspectral remote sensing data analysis and future challenges,

    J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P . Scheunders, N. M. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,”IEEE Geoscience and Remote Sens- ing Magazine, vol. 1, no. 2, pp. 6–36, 2013

    2013

  15. [23]

    Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox,

    B. Rasti, D. Hong, R. Hang, P . Ghamisi, X. Kang, J. Chanussot, and J. A. Benediktsson, “Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox,”IEEE Geoscience and Remote Sensing Magazine, vol. 8, no. 4, pp. 60–88, 2020

    2020

  16. [24]

    A survey of band selection techniques for hyperspectral image classification,

    S. S. Sawant and M. Prabukumar, “A survey of band selection techniques for hyperspectral image classification,”Journal of Spec- tral Imaging, vol. 9, p. a5, 2020

    2020

  17. [25]

    Passive infrared ranging,

    N. K. Leonpacher, “Passive infrared ranging,” Master’s thesis, Air Force Institute of Technology, 1983

    1983

  18. [26]

    Passive ranging using atmospheric oxygen absorp- tion spectra,

    M. R. Hawks, “Passive ranging using atmospheric oxygen absorp- tion spectra,” Ph.D. dissertation, Air Force Institute of Technology, 2006

    2006

  19. [27]

    Passive ranging of dynamic rocket plumes using infrared and visible oxygen attenuation,

    R. A. Vincent and M. R. Hawks, “Passive ranging of dynamic rocket plumes using infrared and visible oxygen attenuation,” in Acquisition, Tracking, Pointing, and Laser Systems Technologies XXV, ser. Proceedings of SPIE, vol. 8052, 2011, p. 80520D

    2011

  20. [28]

    Monocular passive ranging by an optical system with band pass filtering,

    J. R. Anderson, “Monocular passive ranging by an optical system with band pass filtering,” Master’s thesis, Air Force Institute of Technology, 2010

    2010

  21. [29]

    Short- range demonstrations of monocular passive ranging using O 2 absorption spectra,

    M. R. Hawks, R. A. Vincent, J. Martin, and G. P . Perram, “Short- range demonstrations of monocular passive ranging using O 2 absorption spectra,”Applied Spectroscopy, vol. 67, no. 5, pp. 513– 519, 2013

    2013

  22. [30]

    Passive ranging using a filter- based non-imaging method based on oxygen absorption,

    H. Yu, B. Liu, Z. Yan, and Y. Zhang, “Passive ranging using a filter- based non-imaging method based on oxygen absorption,”Applied Optics, vol. 56, no. 28, pp. 7803–7807, 2017

    2017

  23. [31]

    Eleven years of ground–air temperature tracking over different land cover types,

    V . Cermak, L. Bodri, M. Kresl, P . Dedecek, and J. Safanda, “Eleven years of ground–air temperature tracking over different land cover types,”International Journal of Climatology, vol. 37, no. 2, pp. 1084– 1099, 2017

    2017

  24. [32]

    Au- tonomous atmospheric compensation (AAC) of high resolution hyperspectral thermal infrared remote-sensing imagery,

    D. Gu, A. R. Gillespie, A. B. Kahle, and F. D. Palluconi, “Au- tonomous atmospheric compensation (AAC) of high resolution hyperspectral thermal infrared remote-sensing imagery,”IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 6, pp. 2557–2570, 2000

    2000

  25. [33]

    Coupled subspace-based atmospheric compensation of LWIR hyperspectral data,

    N. Acito, M. Diani, and G. Corsini, “Coupled subspace-based atmospheric compensation of LWIR hyperspectral data,”IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 8, pp. 5224–5238, 2019

    2019

  26. [34]

    A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflec- tion radiometer (ASTER) images,

    A. R. Gillespie, S. Rokugawa, T. Matsunaga, J. S. Cothern, S. J. Hook, and A. B. Kahle, “A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflec- tion radiometer (ASTER) images,”IEEE Transactions on Geoscience and Remote Sensing, v...

    1998

  27. [35]

    Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint,

    N. Wang, H. Wu, F. Nerry, C. Li, and Z.-L. Li, “Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint,”IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 4, pp. 1291–1303, 2011

    2011

  28. [36]

    Land surface temperature and emissivity retrieval from field-measured hyperspectral thermal infrared data using wavelet transform,

    Y.-Z. Zhang, H. Wu, X.-G. Jiang, Y.-Z. Jiang, Z.-X. Liu, and F. Nerry, “Land surface temperature and emissivity retrieval from field-measured hyperspectral thermal infrared data using wavelet transform,”Remote Sensing, vol. 9, no. 5, p. 454, 2017

    2017

  29. [37]

    Airborne thermal infrared hyperspectral image temperature and emissivity retrieval based on inter-channel correlated automatic atmospheric compensation and TES,

    D. Wang, L. Cao, L. Gao, and Y. Zhong, “Airborne thermal infrared hyperspectral image temperature and emissivity retrieval based on inter-channel correlated automatic atmospheric compensation and TES,”Remote Sensing of Environment, vol. 315, p. 114410, 2024

    2024

  30. [38]

    Universal computational thermal imaging overcoming the ghosting effect,

    H. Xu, D. Wang, C. Zhao, J. Chen, J. Lin, L. Cao, Y. Zhong, Y. She, and F. Bao, “Universal computational thermal imaging overcoming the ghosting effect,” 2026

    2026

  31. [39]

    HADAR-based thermal infrared hyperspectral image restora- tion,

    C. Dai, J. Lin, B. Song, Y. Chen, J. Chen, X. Yuan, and F. Bao, “HADAR-based thermal infrared hyperspectral image restora- tion,” 2026

    2026

  32. [40]

    A symmetric sparse repre- sentation based band selection method for hyperspectral imagery classification,

    W. Sun, M. Jiang, W. Li, and Y. Liu, “A symmetric sparse repre- sentation based band selection method for hyperspectral imagery classification,”Remote Sensing, vol. 8, no. 3, p. 238, 2016

    2016

  33. [41]

    Band subset selection for hyperspectral image classification,

    C. Yu, M. Song, and C.-I. Chang, “Band subset selection for hyperspectral image classification,”Remote Sensing, vol. 10, no. 1, p. 113, 2018

    2018

  34. [42]

    Unsupervised hyperspectral band selec- tion using spectral–spatial iterative greedy algorithm,

    X. Yang and W. Wang, “Unsupervised hyperspectral band selec- tion using spectral–spatial iterative greedy algorithm,”Sensors, vol. 25, no. 18, p. 5638, 2025

    2025

  35. [43]

    MODTRAN6: A major upgrade of the MOD- TRAN radiative transfer code,

    A. Berk, P . Conforti, R. Kennett, T. Perkins, F. Hawes, and J. van den Bosch, “MODTRAN6: A major upgrade of the MOD- TRAN radiative transfer code,” inAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, vol. 9088. SPIE, 2014, p. 90880H

    2014

  36. [44]

    The HITRAN2020 molecular spectroscopic database,

    I. E. Gordon, L. S. Rothman, R. J. Hargreaves, R. Hashemi, E. V . Karlovets, F. M. Skinner, E. K. Conway, C. Hill, R. V . Kochanov, Y. Tan, P . Wcisło, A. A. Finenko, K. Nelson, P . F. Bernath, M. Birk, V . Boudon, A. Campargue, K. V . Chance, A. Coustenis, B. J. Drouin et al....

    2022

  37. [45]

    The ECOSTRESS spectral library version 1.0,

    S. K. Meerdink, S. J. Hook, D. A. Roberts, and E. A. Abbott, “The ECOSTRESS spectral library version 1.0,”Remote Sensing of Environment, vol. 230, p. 111196, 2019

    2019

  38. [46]

    The ASTER spectral library version 2.0,

    A. M. Baldridge, S. J. Hook, C. I. Grove, and G. Rivera, “The ASTER spectral library version 2.0,”Remote Sensing of Environment, vol. 113, no. 4, pp. 711–715, 2009

    2009

  39. [47]

    Iterative retrieval of surface emissivity and tempera- ture for a hyperspectral sensor,

    C. C. Borel, “Iterative retrieval of surface emissivity and tempera- ture for a hyperspectral sensor,” Los Alamos National Laboratory, Tech. Rep., 1997

    1997

  40. [48]

    S. M. Kay,Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory. Prentice Hall, 1993

    1993

  41. [49]

    Adam: A method for stochastic optimiza- tion,

    D. P . Kingma and J. Ba, “Adam: A method for stochastic optimiza- tion,” inInternational Conference on Learning Representations, 2015

    2015

  42. [50]

    Hyperspectral band selection: A review,

    W. Sun and Q. Du, “Hyperspectral band selection: A review,”IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 2, pp. 118–139, 2019

    2019

  43. [51]

    Sensor selection via convex optimization,

    S. Joshi and S. Boyd, “Sensor selection via convex optimization,” IEEE Transactions on Signal Processing, vol. 57, no. 2, pp. 451–462, 2009

    2009

  44. [52]

    Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies,

    A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies,”Journal of Machine Learning Research, vol. 9, pp. 235–284, 2008

    2008

  45. [53]

    DARPA Invisible Headlights Dataset,

    Kitware, “DARPA Invisible Headlights Dataset,” Registry of Open Data on AWS, 2021, multi-sensor off-road navigation dataset with visible, hyperspectral, polarimetric, broadband, and LiDAR data

    2021

Pith tools

Reviewed July 1, 2026 · model on record in the stance chip above.