Pith. sign in

REVIEW 3 major objections 6 minor 72 references

Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read FairHyp argues that hyperspectral images need dimension-specific modules, not uniform models, and shows gains on four tasks.

desk verdict A solid, above-average HSI architecture study that overclaims at the margins; the modules are new and the ablations are thorough, but the 'consistently outperforms' claim needs error bars and a few text errors should be fixed. read the letter →

arxiv 2505.11267 v1 pith:TIZ7M4JX submitted 2025-05-16 cs.CV cs.AI

Wuzhou Quan , Mingqiang Wei , Jinhui Tang This is my paper · ORCID
classification cs.CVcs.AI
keywords hyperspectralimagefairness-directedrepresentationRunge-Kuttaspatialadaptermulti-receptive-fieldconvolutionstatespacemodelMambadenoisingsuper-resolution
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

The paper claims that hyperspectral images fail the homogeneity assumption most models build on: spatial continuity, spectral correlation, and feature density each vary in different ways, and a single shared processing path handles none of them well. It proposes FairHyp, a modular framework with a Runge-Kutta-inspired spatial adapter, a sparse-aware fair convolution, and a spectral-context state space model, each targeting one form of non-uniformity while being inserted into ordinary classification or restoration pipelines. The paper reports that FairHyp beats state-of-the-art unified models across classification, denoising, super-resolution, and inpainting, and that removing any of the three modules hurts all four tasks. If correct, the claim reframes fairness as a structural requirement of representation learning rather than a property of any particular task or dataset.

What carries the argument

The load-bearing object is the three-module disentanglement inside FairHyp. RK4-SVA adapts the classical fourth-order Runge-Kutta integrator to spatial refinement: four learned residual blocks play the role of the derivative evaluations $k_1,\dots,k_4$, and the weighted combination with coefficients $1/6, 2/6, 2/6, 1/6$ defines the reconstruction update, with a learnable step size $h$ per block. S2FairConv splits input channels into active and passive parts, applies depthwise spatial, spectral, and band-wise projections to a quarter of channels, then recombines them, so capacity is spent where features are informative. SCSS applies forward and backward state space scanning along the band dimension and concatenates the results with mean, max, min, and variance computed over spatial dimensions, then fuses them by a gated projection. The three modules are placed at different pipeline depths so that each addresses its assigned dimension without interfering with the others.

What would settle it

Replace the four RK4 branches' weighted combination with a plain average of $k_1,k_2,k_3,k_4$ of identical total capacity, keeping everything else fixed; if super-resolution and inpainting performance does not drop, the Runge-Kutta structure is not what produces the reported gains. A second check is to train RK4-SVA with the learnable step sizes $h_t$ frozen to equal values and see whether the modulation module alone can compensate.

Watch

Extended reading notes

Core claim

The paper's central claim is that the persistent quality gap in hyperspectral processing comes from a structural flaw, not from a shortage of capacity: spatial, spectral, and feature dimensions need separate treatment. FairHyp embodies this as three modules. RK4-SVA views spatial resolution loss as a degenerate diffusion process governed by $dX/dt = -F(X)$ and inverts it with four learned residual blocks combined by the classical fourth-order Runge-Kutta weights, producing $\hat{Y} = X_t + \frac{1}{6}(k_1+2k_2+2k_3+k_4)$. S2FairConv extracts multi-receptive-field features and actively skips redundant channels to respect spectral sparsity. SCSS scans the spectral dimension forward and backward with a selective state space model and augments it with band-wise statistical descriptors, capturing long-range correlations that local convolutions miss. The paper shows that inserting these modules into two pipeline types improves performance on four tasks, with the largest margins on benchmarks that contain long-range or irregular spectral structure.

Load-bearing premise

The spatial adapter's benefit rests on the assumption that spatial degradation in hyperspectral images behaves like a smooth continuous process captured by a first-order differential equation, so a numerical integration scheme can reverse it; the paper offers no direct evidence that real degradation is smooth or that learned replacements for the derivative function preserve any numerical property.

Editorial extensions

If this is right

  • On the Indian Pines classification benchmark, the transformer-based FairHyp* variant reaches 98.08% overall accuracy, ahead of all compared CNN and transformer baselines.
  • On CAVE ×4 super-resolution, FairHyp reports a 1.16 dB PSNR gain over the second-best method, with similar margins on Chikusei.
  • Ablation across all four tasks shows that removing any single module lowers performance, which the paper reads as evidence that the three non-uniformities are mutually reinforcing rather than redundant.
  • Feature skipping in S2FairConv reduces computation and parameters while slightly improving quality, suggesting that exploiting HSI sparsity is compatible with higher fidelity.
  • The SCSS attention visualizations show forward and backward scanning produce distinct, complementary spectral patterns, supporting the claim that bidirectional spectral context matters.

Reading between the lines

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

  • A natural test of the paper's thesis is to transplant the same three-module scheme to other high-dimensional data with mixed structure, such as multispectral video or medical volume scans; the framework predicts they will show the same non-uniformity pattern.
  • The RK4 weighting is the only formal difference between RK4-SVA and a generic four-branch residual block; an ablation that replaces the $1/6,2/6,2/6,1/6$ coefficients with equal weights would show whether the numerical-integration interpretation carries the gain or merely supplies a convenient initialization.
  • The paper's own band-correlation analysis suggests the optimal spectral receptive field is dataset-dependent; a model that learns the scanning range per image could outperform a fixed bidirectional scan.
  • If non-uniformity is truly intrinsic to HSI structure, the same modules should transfer to unseen sensors and unseen degradation types without retraining the module internals; that is a direct falsifiable prediction of the paper's framing.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes FairHyp, a modular framework for hyperspectral image (HSI) representation that targets three purported forms of non-uniformity: spatial variability, feature sparsity, and spectral irregularity. Three modules are introduced: an RK4-inspired spatial variability adapter (RK4-SVA), a spatial-spectral fair convolution with feature skipping (S2FairConv), and a spectral-context state space model using bidirectional Mamba scanning and statistical aggregation (SCSS). The framework is evaluated on four tasks—classification, denoising, super-resolution, and inpainting—across several datasets, with comparisons against a range of CNN-, Transformer-, and model-based baselines. The paper claims that FairHyp 'consistently outperforms state-of-the-art methods under varied imaging conditions' and that the design redefines fairness as a structural necessity in HSI modeling.

Significance. If the empirical results are reliable, FairHyp is a practically useful set of plug-in components: it shows gains over strong baselines on several tasks and datasets, provides code, and includes a detailed ablation analysis showing that each module contributes to overall performance. The modular design is a reasonable response to the real heterogeneity of HSI data, and the efficiency comparisons are a useful addition. However, the headline claim of consistent superiority is not supported by the paper's own tables, and no uncertainty quantification is provided for any reported result. The theoretical grounding of the RK4-SVA module is also weak, as discussed below. The contribution is therefore a moderately useful empirical architecture rather than a rigorously established new principle.

major comments (3)
  1. [Abstract and Section IV-C, Table III] The central claim that FairHyp 'consistently outperforms state-of-the-art methods' is contradicted by the paper's own results. In Table III, under Gaussian noise at sigma=70, FairHyp reports SSIM 96.04% versus SERT's 96.26%, and SAM 6.32 versus SERT's 6.19 (lower is better). Under blind 30-70 noise, SERT achieves lower SAM (5.02 vs. 5.66). No standard deviations, confidence intervals, or repeated-run results are reported anywhere in the paper, so the small margins (e.g., 0.10-0.67 dB PSNR) cannot be distinguished from run-to-run variation. The claim should be softened to reflect the evidence, and uncertainty quantification should be added for the main comparisons.
  2. [Section III-B, Eqs. (1)-(3)] The RK4-SVA module rests on the premise that spatial degradation follows a first-order ODE, dX/dt = -F(X), and that the classical RK4 integration scheme can invert this degradation. No evidence is given that spatial resolution loss behaves as such an ODE, that F is smooth enough for RK4 to apply, or that the learned step sizes h2-h4 preserve any property of the RK4 method. As presented, the module is effectively a four-branch residual block with learnable interpolation weights; without justification, the claim that it 'restores spatial coherence' or inherits numerical guarantees from RK4 is unsupported. Please either provide theoretical or empirical support for the ODE model or explicitly describe the module as an RK4-inspired residual architecture rather than a numerical integrator.
  3. [Section III-B, Eqs. (4)-(5)] The reference-aware modulation is defined circularly. Eq. (4) defines alpha = sigma(G(Y_hat, s)), while Eq. (5) defines G(Y_hat, s) = Y_hat * (1 + alpha) with alpha = sigma(A([Y_hat, s])). Since alpha appears on both sides, the definition is ambiguous and not implementable as written. Please rewrite the equations to give a well-defined computational graph, e.g., by first computing a gating map and then applying it, or by explicitly describing an iterative or sequential procedure.
minor comments (6)
  1. [Abstract] Typo: 'inpaintin' should be 'inpainting'.
  2. [Index Terms] The index terms list 'Infrared small target detection', which is unrelated to the content of this paper; these terms should be corrected.
  3. [Section IV-E] The cross-reference states that Chikusei and ICVL were introduced in 'Section IV-B and Section IV-C, respectively,' but Chikusei is introduced in Section IV-D (super-resolution), not Section IV-B.
  4. [Section IV-D, Table IV] The text refers to 'the Indian Pines dataset' when reporting super-resolution results, but the experimental setting in this section uses the Chikusei dataset, and Table IV labels the second dataset as 'Chikusei'. Please correct the text to match the table.
  5. [Section III-B, Eq. (3)] The variable X_t appears in Eq. (3) without prior definition. It should be clarified that X_t denotes the input X_SVA at the current state.
  6. [Section IV-B] The relationship between 'FairHyp' and 'FairHyp*' should be stated more precisely in the main text; the abstract and conclusion refer to FairHyp as a single method, while the experiments distinguish two variants.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical improvements are benchmarked externally, and the Eq. (4)-(5) self-reference is a notational redundancy, not a derivation.

full rationale

FairHyp's central claim is an empirical one: the three modules, inserted into restoration and classification pipelines, outperform external baselines on held-out test sets. The derivation chain is therefore anchored outside the paper: none of the modules is fitted to a subset of the evaluation data and then presented as a prediction of a closely related quantity; the ablations compare trained configurations on test sets, and the comparative tables report against independent published methods and datasets. The RK4-SVA module uses the RK4 equations only as a template: the functions F1..F4 are learned residual blocks and the step sizes h_t are learnable scalars, so the paper does not derive a numerical guarantee or a physical quantity from the ODE premise, and no result is equivalent to its input by construction. The only formally self-referential passage is Section III-B, Eqs. (4)-(5), where alpha is written as sigma(G(Yhat,s)) and G is then defined as Yhat*(1+alpha); read literally this is a notational loop, but the same passage supplies the intended feed-forward definition alpha = sigma(A([Yhat⊕s])), and no empirical claim or prediction is derived from this loop, so it is a notation error rather than a circular derivation. The paper contains no load-bearing self-citations and does not invoke a uniqueness theorem from the authors' prior work; the closest cited priors (RK4, FasterNet, Mamba-style scanning) are external and used as architectural inspiration. Concerns raised about Table III counterexamples and absent error bars concern correctness and statistical support, not circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The framework rests on three domain assumptions about HSI structure, one ad hoc ODE model for spatial degradation, and two hand-chosen design constants. The central empirical claim does not depend on a fitted parameter that is later reported as a prediction, but the conceptual framing is not independently evidenced.

free parameters (2)
  • Learnable RK4 step sizes h2, h3, h4 = learned, not reported
    Eq. (3) replaces the single RK4 step size with three learnable scalars controlling update magnitude. Their fitted values are never reported, and since the four F_t blocks are also learned, the RK4 numerical interpretation is not constrained.
  • Channel skip ratio in S2FairConv = 1/4 of channels
    Section III-C states that only one-fourth of channels are actively projected in each path. This hand-chosen ratio is not swept in the main experiments; Table X varies channel dimension but not the skip ratio.
assumptions (4)
  • ad hoc to paper Spatial degradation follows a first-order ODE dX/dt = -F(X) with a smooth enough F for RK4 to apply.
    Introduced in Section III-B, Eq. (1). No evidence is given that HSI spatial variability is generated by such an ODE, and F is an arbitrary learned CNN, so classical RK4 convergence guarantees do not transfer.
  • domain assumption HSI features are inherently low-rank and sparse, so skipping 75 percent of channels preserves or improves representation.
    Invoked in Section III-C and Section I. This is a widely held but empirical claim about HSI structure; if false, the S2FairConv selective path would discard useful information.
  • domain assumption The threefold decomposition of non-uniformity into spatial variability, feature sparsity, and spectral irregularity is exhaustive and non-overlapping.
    Section I frames non-uniformity as exactly these three aspects. The paper does not test whether other forms of non-uniformity, such as noise correlations or label imbalance, are captured, or whether the modules interfere.
  • domain assumption Bidirectional Mamba scanning plus spatial statistics is sufficient for long-range spectral modeling without introducing spatial bias.
    Section III-D assumes selective spectral scanning and band-wise statistics capture spectral dependencies with minimal spatial interference. Only attention visualizations are offered as evidence, not a quantitative isolation of spatial contamination.
invented entities (1)
  • The HSI representation trilemma
    purpose: Conceptual motivation claiming spatial adaptability, spectral long-range modeling, and feature efficiency are in conflict for existing models.
    Fig. 1 and Section I introduce this triangle as a motivating structure, but no quantitative measure of the trade-off is provided. It functions as a narrative device rather than a measured constraint.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity." pith.science (2026). https://pith.science/paper/TIZ7M4JX

@misc{pith2026250511267,
  author       = {Pith},
  title        = {Pith review of: Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TIZ7M4JX}},
  note         = {Machine review of arXiv:2505.11267}
}
read the original abstract

Hyperspectral image (HSI) representation is fundamentally challenged by pervasive non-uniformity, where spectral dependencies, spatial continuity, and feature efficiency exhibit complex and often conflicting behaviors. Most existing models rely on a unified processing paradigm that assumes homogeneity across dimensions, leading to suboptimal performance and biased representations. To address this, we propose FairHyp, a fairness-directed framework that explicitly disentangles and resolves the threefold non-uniformity through cooperative yet specialized modules. We introduce a Runge-Kutta-inspired spatial variability adapter to restore spatial coherence under resolution discrepancies, a multi-receptive field convolution module with sparse-aware refinement to enhance discriminative features while respecting inherent sparsity, and a spectral-context state space model that captures stable and long-range spectral dependencies via bidirectional Mamba scanning and statistical aggregation. Unlike one-size-fits-all solutions, FairHyp achieves dimension-specific adaptation while preserving global consistency and mutual reinforcement. This design is grounded in the view that non-uniformity arises from the intrinsic structure of HSI representations, rather than any particular task setting. To validate this, we apply FairHyp across four representative tasks including classification, denoising, super-resolution, and inpaintin, demonstrating its effectiveness in modeling a shared structural flaw. Extensive experiments show that FairHyp consistently outperforms state-of-the-art methods under varied imaging conditions. Our findings redefine fairness as a structural necessity in HSI modeling and offer a new paradigm for balancing adaptability, efficiency, and fidelity in high-dimensional vision tasks.

Figures

Figures reproduced from arXiv: 2505.11267 by the authors.

Figure 1
Figure 1. Illustration of the trilemma in hyperspectral image [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Integration of proposed modules into two representative HSI pipelines. RK4-SVA, S [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Spatial variability across imaging sources. Different [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Visualization revealing the breakdown of adjacency [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Schematic of the RK4-based Spatial Variability [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Comparisons of the classification visual results of different methods on the [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Comparisons of the classification visual results of different methods on the [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Comparisons of the denoising visual results of different methods on the [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Comparisons of the denoising visual results of different methods on the [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: Comparisons of the super-resolution visual results of different methods on the [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Comparisons of the inpainting visual results of different methods on the [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: Comparison of classification performance, model size, [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]
Figure 13
Figure 13. Figure 13: Visualization of spectral context modeling in [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

72 extracted references · 65 canonical work pages

  1. [1]

    Medical hyperspectral imaging: a review,

    G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt., vol. 19, no. 1, p. 010901, 2014

    work page 2014

  2. [2]

    Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innova- tion and prospects,

    C. Liu, C. Xing, Q. Hu, S. Wang, S. Zhao, and M. Gao, “Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innova- tion and prospects,” Earth Sci. Rev., vol. 226, p. 103958, 2022

    work page 2022

  3. [3]

    Hyperspectral remote sensing in litho- logical mapping, mineral exploration, and environmental geology: an updated review,

    S. Peyghambari and Y . Zhang, “Hyperspectral remote sensing in litho- logical mapping, mineral exploration, and environmental geology: an updated review,” J. Appl. Remote Sens. , vol. 15, no. 3, p. 031501, 2021

    work page 2021

  4. [4]

    A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects,

    B. G. Ram, P. Oduor, C. Igathinathane, K. Howatt, and X. Sun, “A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects,” Comput. Electron. Agr., vol. 222, p. 109037, 2024

    work page 2024

  5. [5]

    Application of hyperspectral imaging in food safety inspection and control: A review,

    Y .-Z. Feng and D.-W. Sun, “Application of hyperspectral imaging in food safety inspection and control: A review,” Crit. Rev. Food Sci. , vol. 52, no. 11, pp. 1039–1058, 2012

    work page 2012

  6. [6]

    Hyperspectral imaging for military and security applications: Combining myriad processing and sensing techniques,

    M. Shimoni, R. Haelterman, and C. Perneel, “Hyperspectral imaging for military and security applications: Combining myriad processing and sensing techniques,” IEEE Geosci. Remote Sens. Mag. , vol. 7, no. 2, pp. 101–117, 2019

    work page 2019

  7. [7]

    Tensor cascaded-rank minimization in subspace: A unified regime for hyperspectral image low-level vision,

    L. Sun, C. He, Y . Zheng, Z. Wu, and B. Jeon, “Tensor cascaded-rank minimization in subspace: A unified regime for hyperspectral image low-level vision,” IEEE Trans. Image Process. , vol. 32, pp. 100–115, 2023

    work page 2023

  8. [8]

    Non-local meets global: An iterative paradigm for hyperspectral image restoration,

    W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao, H. Zhang, and L. Zhang, “Non-local meets global: An iterative paradigm for hyperspectral image restoration,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 44, no. 4, pp. 2089–2107, 2022

    work page 2022

Show all 72 references
  1. [9]

    Hyperspectral image classification using groupwise separable convolutional vision transformer network,

    Z. Zhao, X. Xu, S. Li, and A. Plaza, “Hyperspectral image classification using groupwise separable convolutional vision transformer network,” IEEE Trans. Geosci. Remote Sens. , vol. 62, pp. 1–17, 2024

    2024

  2. [10]

    Diverse region-based cnn for hyperspec- tral image classification,

    M. Zhang, W. Li, and Q. Du, “Diverse region-based cnn for hyperspec- tral image classification,” IEEE Trans. Image Process. , vol. 27, no. 6, pp. 2623–2634, 2018

    2018

  3. [11]

    Dual-stage hyperspectral image classification model with spectral supertoken,

    P. Liu, T. Xu, J. Wang, H. Chen, H. Bai, and J. Li, “Dual-stage hyperspectral image classification model with spectral supertoken,” in Proc. Eur. Conf. Comput. Vis. (ECCV), A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, and G. Varol, Eds. Cham: Springer Nature Sw...

    2025

  4. [12]

    Deep spatial-spectral global reasoning network for hyperspectral image denoising,

    X. Cao, X. Fu, C. Xu, and D. Meng, “Deep spatial-spectral global reasoning network for hyperspectral image denoising,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–14, 2022

    2022

  5. [13]

    Deep diversity- enhanced feature representation of hyperspectral images,

    J. Hou, Z. Zhu, J. Hou, H. Liu, H. Zeng, and D. Meng, “Deep diversity- enhanced feature representation of hyperspectral images,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 46, no. 12, pp. 8123–8138, 2024

    2024

  6. [14]

    Spectral enhanced rectangle transformer for hyperspectral image denoising,

    M. Li, J. Liu, Y . Fu, Y . Zhang, and D. Dou, “Spectral enhanced rectangle transformer for hyperspectral image denoising,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , June 2023, pp. 5805– 5814. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 17

    2023

  7. [15]

    Waveformer: Spec- tral–spatial wavelet transformer for hyperspectral image classification,

    M. Ahmad, U. Ghous, M. Usama, and M. Mazzara, “Waveformer: Spec- tral–spatial wavelet transformer for hyperspectral image classification,” IEEE Geosci. Remote Sens. Lett. , vol. 21, pp. 1–5, 2024

    2024

  8. [16]

    Physics- inspired degradation models for hyperspectral image fusion,

    J. Lian, L. Wang, L. Zhu, R. Dian, Z. Xiong, and H. Huang, “Physics- inspired degradation models for hyperspectral image fusion,” 2024. [Online]. Available: https://arxiv.org/abs/2402.02411

    2024 arXiv

  9. [17]

    Hybrid spectral denoising transformer with guided attention,

    Z. Lai, C. Yan, and Y . Fu, “Hybrid spectral denoising transformer with guided attention,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) , October 2023, pp. 13 065–13 075

    2023

  10. [18]

    Deep learning and process understanding for data-driven earth system science,

    M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and Prabhat, “Deep learning and process understanding for data-driven earth system science,” Nature, vol. 566, no. 7743, pp. 195–204, 2019

    2019

  11. [19]

    Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities,

    L. Zhang and L. Zhang, “Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities,” IEEE Geosci. Remote Sens. Mag. , vol. 10, no. 2, pp. 270–294, June 2022

    2022

  12. [20]

    Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,

    J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 5, no. 2, pp. 354–379, 2012

    2012

  13. [21]

    A review of nonlinear hyperspec- tral unmixing methods,

    R. Heylen, M. Parente, and P. Gader, “A review of nonlinear hyperspec- tral unmixing methods,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 1844–1868, 2014

    2014

  14. [22]

    A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing,

    G. Duveiller and P. Defourny, “A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing,” Remote Sens. Environ., vol. 114, no. 11, pp. 2637–2650, 2010

    2010

  15. [23]

    3-d quasi-recurrent neural network for hyperspectral image denoising,

    K. Wei, Y . Fu, and H. Huang, “3-d quasi-recurrent neural network for hyperspectral image denoising,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 1, pp. 363–375, 2021

    2021

  16. [24]

    Exploring the relationship between 2d/3d convolution for hyperspectral image super-resolution,

    Q. Li, Q. Wang, and X. Li, “Exploring the relationship between 2d/3d convolution for hyperspectral image super-resolution,” IEEE Trans. Geosci. Remote Sens. , vol. 59, no. 10, pp. 8693–8703, 2021

    2021

  17. [25]

    Hyperspectral image superresolution using spectrum and feature context,

    Q. Wang, Q. Li, and X. Li, “Hyperspectral image superresolution using spectrum and feature context,” IEEE Trans. Ind. Electron. , vol. 68, no. 11, pp. 11 276–11 285, 2020

    2020

  18. [26]

    Multimodal hyperspectral unmixing: Insights from attention networks,

    Z. Han, D. Hong, L. Gao, J. Yao, B. Zhang, and J. Chanussot, “Multimodal hyperspectral unmixing: Insights from attention networks,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–13, 2022

    2022

  19. [27]

    Subpixel spectral variability network for hyperspectral image classification,

    Z. Han, J. Yang, L. Gao, Z. Zeng, B. Zhang, and J. Chanussot, “Subpixel spectral variability network for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. , vol. 63, pp. 1–14, 2025

    2025

  20. [28]

    Hyperspectral image classifi- cation via cascaded spatial cross-attention network,

    B. Zhang, Y . Chen, S. Xiong, and X. Lu, “Hyperspectral image classifi- cation via cascaded spatial cross-attention network,” IEEE Trans. Image Process., vol. 34, pp. 899–913, 2025

    2025

  21. [29]

    Very fine spatial resolution urban land cover mapping using an explicable sub-pixel mapping network based on learnable spatial correlation,

    D. He, Q. Shi, J. Xue, P. M. Atkinson, and X. Liu, “Very fine spatial resolution urban land cover mapping using an explicable sub-pixel mapping network based on learnable spatial correlation,” Remote Sens. Environ., vol. 299, p. 113884, 2023

    2023

  22. [30]

    Hy- perspectral unmixing using transformer network,

    P. Ghosh, S. K. Roy, B. Koirala, B. Rasti, and P. Scheunders, “Hy- perspectral unmixing using transformer network,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–16, 2022

    2022

  23. [31]

    Specat: Spatial-spectral cumulative-attention transformer for high-resolution hyperspectral image reconstruction,

    Z. Yao, S. Liu, X. Yuan, and L. Fang, “Specat: Spatial-spectral cumulative-attention transformer for high-resolution hyperspectral image reconstruction,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recog- nit. (CVPR), June 2024, pp. 25 368–25 377

    2024

  24. [32]

    Hyperspectral image super-resolution via recurrent feedback embedding and spatial–spectral consistency regular- ization,

    X. Wang, J. Ma, and J. Jiang, “Hyperspectral image super-resolution via recurrent feedback embedding and spatial–spectral consistency regular- ization,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–13, 2022

    2022

  25. [33]

    Deep posterior distribution-based embedding for hyperspectral image super-resolution,

    J. Hou, Z. Zhu, J. Hou, H. Zeng, J. Wu, and J. Zhou, “Deep posterior distribution-based embedding for hyperspectral image super-resolution,” IEEE Trans. Image Process. , vol. 31, pp. 5720–5732, 2022

    2022

  26. [34]

    Hyperspectral image denoising via sparse representation and low-rank constraint,

    Y .-Q. Zhao and J. Yang, “Hyperspectral image denoising via sparse representation and low-rank constraint,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 296–308, Jan 2015

    2015

  27. [35]

    Spectral unmixing via data-guided sparsity,

    F. Zhu, Y . Wang, B. Fan, S. Xiang, G. Meng, and C. Pan, “Spectral unmixing via data-guided sparsity,”IEEE Trans. Image Process., vol. 23, no. 12, pp. 5412–5427, Dec 2014

    2014

  28. [36]

    Low-rank transformer for high-resolution hyperspectral computational imaging,

    Y . Liu, R. Dian, and S. Li, “Low-rank transformer for high-resolution hyperspectral computational imaging,” Int. J. Comput. Vision , vol. 133, no. 2, pp. 809–824, 2025

    2025

  29. [37]

    Hyperspectral compressive snapshot reconstruction via coupled low-rank subspace representation and self-supervised deep network,

    Y . Chen, W. Lai, W. He, X.-L. Zhao, and J. Zeng, “Hyperspectral compressive snapshot reconstruction via coupled low-rank subspace representation and self-supervised deep network,” IEEE Trans. Image Process., vol. 33, pp. 926–941, 2024

    2024

  30. [38]

    Hsi-denet: Hyperspectral image restoration via convolutional neural network,

    Y . Chang, L. Yan, H. Fang, S. Zhong, and W. Liao, “Hsi-denet: Hyperspectral image restoration via convolutional neural network,”IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 2, pp. 667–682, 2019

    2019

  31. [39]

    A 3-d atrous convolution neural network for hyperspectral image denoising,

    W. Liu and J. Lee, “A 3-d atrous convolution neural network for hyperspectral image denoising,” IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 8, pp. 5701–5715, 2019

    2019

  32. [40]

    Learning spatial-spectral prior for super-resolution of hyperspectral imagery,

    J. Jiang, H. Sun, X. Liu, and J. Ma, “Learning spatial-spectral prior for super-resolution of hyperspectral imagery,” IEEE Trans. Comput. Imaging, vol. 6, pp. 1082–1096, 2020

    2020

  33. [41]

    Spectral–spatial feature tokenization transformer for hyperspectral image classification,

    L. Sun, G. Zhao, Y . Zheng, and Z. Wu, “Spectral–spatial feature tokenization transformer for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–14, 2022

    2022

  34. [42]

    Isnet: Shape matters for infrared small target detection,

    M. Zhang, R. Zhang, Y . Yang, H. Bai, J. Zhang, and J. Guo, “Isnet: Shape matters for infrared small target detection,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , June 2022, pp. 877–886

    2022

  35. [43]

    Dual ode: Spatial–spectral neural ordinary differential equations for hyperspectral image super-resolution,

    X. Zhang, C. Song, T. You, Q. Bai, W. Wei, and L. Zhang, “Dual ode: Spatial–spectral neural ordinary differential equations for hyperspectral image super-resolution,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–15, 2024

    2024

  36. [44]

    Ghostnet: More features from cheap operations,

    K. Han, Y . Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, “Ghostnet: More features from cheap operations,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2020, pp. 1580–1589

    2020

  37. [45]

    Split to be slim: An overlooked redundancy in vanilla convolution,

    Q. Zhang, Z. Jiang, Q. Lu, J. Han, Z. Zeng, S.-H. Gao, and A. Men, “Split to be slim: An overlooked redundancy in vanilla convolution,” arXiv preprint arXiv:2006.12085 , 2020

    2006 arXiv

  38. [46]

    Run, don’t walk: chasing higher flops for faster neural networks,

    J. Chen, S.-h. Kao, H. He, W. Zhuo, S. Wen, C.-H. Lee, and S.-H. G. Chan, “Run, don’t walk: chasing higher flops for faster neural networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2023, pp. 12 021–12 031

    2023

  39. [47]

    Image quality assessment: from error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004

    2004

  40. [48]

    Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm,

    R. H. Yuhas, A. F. Goetz, and J. W. Boardman, “Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm,” in Summ. 3rd Annu. JPL Airborne Geosci. Workshop, Vol. 1, AVIRIS Workshop, 1992

    1992

  41. [49]

    A coefficient of agreement for nominal scales,

    J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas., vol. 20, no. 1, pp. 37–46, 1960

    1960

  42. [50]

    Zhong, X

    Y . Zhong, X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang, “Whu-hi: Uav-borne hyperspectral with high spatial resolution (h2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with crf,” Remote Sens. Environ., vol. 250,...

    2020

  43. [51]

    Decoupled weight decay regularization,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101 , 2017

    2017 arXiv

  44. [52]

    Contextual deep cnn based hyperspectral clas- sification,

    H. Lee and H. Kwon, “Contextual deep cnn based hyperspectral clas- sification,” in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS) , 2016, pp. 3322–3325

    2016

  45. [53]

    3-d deep learning approach for remote sensing image classification,

    A. Ben Hamida, A. Benoit, P. Lambert, and C. Ben Amar, “3-d deep learning approach for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. , vol. 56, no. 8, pp. 4420–4434, 2018

    2018

  46. [54]

    Hyperspectral image transformer classification networks,

    X. Yang, W. Cao, Y . Lu, and Y . Zhou, “Hyperspectral image transformer classification networks,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–15, 2022

    2022

  47. [55]

    Spectralformer: Rethinking hyperspectral image classification with transformers,

    D. Hong, Z. Han, J. Yao, L. Gao, B. Zhang, A. Plaza, and J. Chanus- sot, “Spectralformer: Rethinking hyperspectral image classification with transformers,” IEEE Trans. Geosci. Remote Sens. , vol. 60, pp. 1–15, 2022

    2022

  48. [56]

    Dual selective fusion transformer network for hyperspectral image classification,

    Y . Xu, D. Wang, L. Zhang, and L. Zhang, “Dual selective fusion transformer network for hyperspectral image classification,” Neural Netw., vol. 187, p. 107311, 2025

    2025

  49. [57]

    Sparse recovery of hyperspectral signal from natural rgb images,

    B. Arad and O. Ben-Shahar, “Sparse recovery of hyperspectral signal from natural rgb images,” in Proc. Eur. Conf. Comput. Vis. (ECCV) . Springer, 2016, pp. 19–34

    2016

  50. [58]

    Hyperspectral unmixing: Ground truth labeling, datasets, benchmark performances and survey,

    F. Zhu, “Hyperspectral unmixing: Ground truth labeling, datasets, benchmark performances and survey,” 2017. [Online]. Available: https: //arxiv.org/abs/1708.05125

    2017 arXiv

  51. [59]

    A trainable spectral-spatial sparse coding model for hyperspectral image restora- tion,

    T. Bodrito, A. Zouaoui, J. Chanussot, and J. Mairal, “A trainable spectral-spatial sparse coding model for hyperspectral image restora- tion,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , M. Ranzato, A. Beygelzimer, Y . Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. ...

    2021

  52. [60]

    APEX – the hyperspectral esa airborne prism experiment,

    K. Itten, F. Dell’Endice, A. Hueni, M. Kneub ¨uhler, D. Schl ¨apfer, D. Odermatt, F. Seidel, S. Huber, J. Schopfer, T. Kellenberger et al. , “APEX – the hyperspectral esa airborne prism experiment,” Sensors- basel., vol. 8, no. 10, pp. 6235–6259, 2008

    2008

  53. [61]

    Nonlocal transform-domain filter for volumetric data denoising and reconstruc- tion,

    M. Maggioni, V . Katkovnik, K. Egiazarian, and A. Foi, “Nonlocal transform-domain filter for volumetric data denoising and reconstruc- tion,” IEEE Trans. Image Process. , vol. 22, no. 1, pp. 119–133, Jan 2013. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 18

    2013

  54. [62]

    Hyperspectral image restoration using low-rank matrix recovery,

    H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 4729–4743, Aug 2014

    2014

  55. [63]

    Spatial-spectral transformer for hyperspec- tral image denoising,

    M. Li, Y . Fu, and Y . Zhang, “Spatial-spectral transformer for hyperspec- tral image denoising,” Proc. AAAI Conf. Artif. Intell. , vol. 37, no. 1, pp. 1368–1376, Jun. 2023

    2023

  56. [64]

    Generalized Assorted Pixel Camera: Post-Capture Control of Resolution, Dynamic Range and Spectrum,

    F. Yasuma, T. Mitsunaga, D. Iso, and S. Nayar, “Generalized Assorted Pixel Camera: Post-Capture Control of Resolution, Dynamic Range and Spectrum,” Tech. Rep., Nov 2008

    2008

  57. [65]

    Airborne hyperspectral data over chikusei,

    N. Yokoya and A. Iwasaki, “Airborne hyperspectral data over chikusei,” Space Application Laboratory, University of Tokyo, Japan, Tech. Rep. SAL-2016-05-27, May 2016

    2016

  58. [66]

    Mixed 2d/3d convolutional network for hyperspectral image super-resolution,

    Q. Li, Q. Wang, and X. Li, “Mixed 2d/3d convolutional network for hyperspectral image super-resolution,” Remote sensing, vol. 12, no. 10, p. 1660, 2020

    2020

  59. [67]

    Bidirectional 3d quasi-recurrent neural network for hyperspectral image super-resolution,

    Y . Fu, Z. Liang, and S. You, “Bidirectional 3d quasi-recurrent neural network for hyperspectral image super-resolution,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 14, pp. 2674–2688, 2021

    2021

  60. [68]

    Essaformer: Efficient transformer for hyperspectral image super- resolution,

    M. Zhang, C. Zhang, Q. Zhang, J. Guo, X. Gao, and J. Zhang, “Essaformer: Efficient transformer for hyperspectral image super- resolution,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), October 2023, pp. 23 073–23 084

    2023

  61. [69]

    Deep blind super-resolution for hyperspectral images,

    P. Yang, Y . Ma, X. Mei, Q. Chen, M. Wu, and J. Ma, “Deep blind super-resolution for hyperspectral images,” Pattern Recogn., vol. 157, p. 110916, 2025

    2025

  62. [70]

    Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations,

    L. Zhuang and J. M. Bioucas-Dias, “Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 11, no. 3, pp. 730– 742, March 2018

    2018

  63. [71]

    Deep hyperspectral prior: Single- image denoising, inpainting, super-resolution,

    O. Sidorov and J. Yngve Hardeberg, “Deep hyperspectral prior: Single- image denoising, inpainting, super-resolution,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV) Workshops , Oct 2019

    2019

  64. [72]

    Weighted low-rank tensor recovery for hyperspectral image restora- tion,

    Y . Chang, L. Yan, X.-L. Zhao, H. Fang, Z. Zhang, and S. Zhong, “Weighted low-rank tensor recovery for hyperspectral image restora- tion,” IEEE Trans. Cybern., vol. 50, no. 11, pp. 4558–4572, Nov 2020. Wuzhou Quan received the B.E. degree in Elec- tronic Information Engineerin...

    2014

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.