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REVIEW 3 major objections 6 minor 65 references

MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read MambaHSI claims to be the first image-level, Mamba-based hyperspectral classifier, reaching state-of-the-art accuracy on four benchmarks with linear computational complexity.

desk verdict Useful empirical Mamba-for-HSI paper; the spatial long-range claim is untested because of a causal raster scan with no positional encoding, and the Mamba baselines are missing—still worth reviewing after experiments. read the letter →

arxiv 2501.04944 v1 pith:DFAEO3DJ submitted 2025-01-09 cs.CV

Yapeng Li , Yong Luo , Lefei Zhang , Zengmao Wang , Bo Du This is my paper · ORCID
classification cs.CV
keywords hyperspectralimageclassificationstatespacemodelsMambaspatial-spectralfusionimage-levellinearcomputationalcomplexitylong-rangedependencies
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 proposes MambaHSI, a hyperspectral image classifier built entirely from state-space layers, and argues that this design can model long-range spatial and spectral dependencies while scaling linearly with the number of pixels. It claims this is the first image-level, Mamba-based HSI classification model, taking the whole image as input rather than patches, and reports that it outperforms the ML, GCN, CNN, and Transformer baselines on the Pavia University, Houston, HanChuan, and HongHu datasets. The motivating point is that patch-based Transformers pay quadratic attention cost and lose pixel-level detail, while CNNs cannot reach far enough spatially. If the claim holds, whole-scene hyperspectral classification becomes feasible at pixel resolution with modest compute.

What carries the argument

The load-bearing object is the Mamba layer, a selective state-space sequence model that processes a one-dimensional sequence through a hidden state with input-dependent gating, in time linear in sequence length. MambaHSI applies it twice: the spatial Mamba block flattens the image into a raster scan of pixels to capture spatial context, and the spectral Mamba block partitions the spectral dimension into $G$ groups and treats the groups as a sequence to capture spectral continuity. The fusion module computes $H_{fus} = H_i + w_{spa}H_{spa}^o + w_{spe}H_{spe}^o$ with learned scalar weights $w_{spa}$ and $w_{spe}$, and residual connections are added throughout. The whole encoder is built only from these blocks, which is what makes the claimed linear complexity and end-to-end whole-image training possible.

What would settle it

Train the spatial-only branch (or the full model) with two different fixed pixel orderings, for example normal raster order versus a random but fixed permutation of all pixels, and compare per-pixel accuracy on the same training and test splits. If accuracy does not change, the spatial branch is not actually using spatial adjacency; if accuracy collapses, the reported gains depend on the specific arbitrary ordering rather than on a learned spatial representation.

Watch

Extended reading notes

Core claim

The central claim is that a pure state-space (Mamba) backbone, with separate spatial and spectral branches and an adaptive fusion step, gives the best hyperspectral classification accuracy on four diverse benchmarks. The spatial branch flattens the entire image into a one-dimensional sequence of pixel embeddings and runs a Mamba layer over it; the spectral branch splits each pixel's spectrum into groups, orders the groups, and runs a Mamba layer across them; the fusion module learns two scalar weights to combine the branches. The paper reports that this arrangement surpasses all compared ML-, GCN-, CNN-, and Transformer-based methods on all four datasets, with a single forward pass over the whole image and computational complexity that scales linearly in pixel count.

Load-bearing premise

The model's spatial power rests on the assumption that reading the whole image as one flat line of pixels, in fixed raster order with no positional encoding and no scan-direction design, still lets the Mamba layer learn genuinely spatial long-range relationships.

Editorial extensions

If this is right

  • Whole-scene hyperspectral classification can be performed in a single forward pass with complexity $O(L)$ in the number of pixels, making very large images tractable.
  • Pixel-level tokens, rather than patches, improve classification accuracy; the paper shows that increasing patch size degrades performance on the Pavia University dataset.
  • Modeling the spectrum as an ordered sequence of groups helps classification; setting the group count to one, which removes spectral ordering, drops overall accuracy by roughly 5.6 points on Pavia University.
  • Adaptive fusion of spatial and spectral branches with learned scalar weights outperforms simple summation, especially on the HongHu dataset, where it adds about 5.5 points of overall accuracy.

Reading between the lines

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

  • Beyond the paper, a natural next test is whether the raster-scan ordering actually matters: a multi-directional or bidirectional scan variant might improve accuracy further, since the paper does not ablate the scan direction.
  • The learned fusion weights $w_{spa}$ and $w_{spe}$ are not reported; inspecting them per dataset could reveal whether the spatial branch dominates in every scene or whether the balance shifts with ground-object scale.
  • The same two-branch state-space design could transfer to other dense prediction tasks, such as multispectral land-cover mapping or semantic segmentation, where linear scaling with image size is a practical advantage.
  • A stronger control experiment would shuffle pixels with a fixed random permutation at training time; the paper does not run this test, so the attribution of gains to 'long-range spatial modeling' remains unverified.
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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 MambaHSI, an image-level hyperspectral image classification architecture built entirely on state space models. Each pixel is embedded individually; a spatial Mamba block processes all pixels as a rasterized 1D sequence, a spectral Mamba block processes grouped spectral vectors, and a spatial-spectral fusion module combines the two branches with learned scalar weights. The authors claim that this is the first image-level SSM-based HSI classifier, that it models long-range interactions of the whole image at pixel level with linear complexity, and that it outperforms ML-, GCN-, CNN-, and Transformer-based baselines on Pavia University, Houston, HanChuan, and HongHu. The experimental section provides ten-run comparisons, ablations, hyperparameter studies, and a scaling experiment comparing Mamba with self-attention.

Significance. If the empirical results hold, the paper is a useful contribution: an image-level, linear-complexity backbone for HSI classification with public code and evaluation on four datasets. The complexity scaling experiment in Table VIII is a clean demonstration of linear versus quadratic behavior, and the ablation and hyperparameter studies are thorough. The significance is currently limited by the lack of validation of the spatial long-range mechanism, the absence of any Mamba-based baseline in the comparison, and the lack of statistical significance tests, so the 'next-generation backbone' claim is not yet established.

major comments (3)
  1. [Section IV-C, Eq. (7), and Section V-B] The spatial Mamba block flattens the entire 2D image into a raster-scan 1D sequence and applies a standard causal Mamba without positional encoding. Because the scan is causal, token t cannot see any later token, so no pixel receives information from its right or lower neighbors; without coordinate information, the 2D arrangement is only an arbitrary ordering. The paper's recurring claim that SpaMB models 'long-range interaction of the whole image at the pixel level' is therefore not established by the architecture. The reported gains could plausibly come from the spectral branch, the fusion module, or a global bag-of-pixels effect. Please provide a control experiment, for example randomizing the pixel permutation, using bidirectional or multi-directional scans, or replacing SpaMB with a non-spatial sequence model of the same capacity, and show that the spatial ordering specifically is responsible for the accuracy gain.
  2. [Section V-B, Tables I and V] No statistical significance tests are reported. On PaviaU, MambaHSI's OA is 95.74±0.90 versus 94.97±1.43 for CLOLN, a difference of 0.77 percentage points, which is within one standard deviation of both means. The claim that the model 'outperforms all ... and achieves SOTA on all datasets' is too strong for this dataset. Please report paired tests (for example, McNemar's test or pairwise comparisons across the ten trials) and adjust the wording accordingly, or justify that the differences are meaningful.
  3. [Section II.B and Section V.A comparison list] The related work cites several Mamba-based HSI classifiers, including SpectralMamba [57] and 3DSS-Mamba [58], but none of these are included in the experimental comparison. Because the paper's central contribution is a Mamba architecture for HSI, the absence of any Mamba baseline makes the 'superiority' claim incomplete. Please add at least one patch-level Mamba method evaluated under the same training/validation split, or explain with runtime and accuracy evidence why the image-level comparison omits them; in either case, the SOTA statement should be qualified to 'among the compared methods'.
minor comments (6)
  1. [Section III, Eq. (2)] The notation A and B is reused for both the continuous and discrete parameters; please disambiguate these symbols.
  2. [Section IV.D and other places] There are several grammatical slips, including 'we divide a spectral features into G group' and 'the semantic vector'; please correct these throughout.
  3. [Table VIII] The table header and row labels do not clearly indicate which row corresponds to the Mamba encoder block and which to the self-attention variant; please add explicit row labels.
  4. [Section V-F, Table IX] The 'w SSI' OA on PaviaU is 80.67±2.07, while the SpeMB row in Table V reports 78.34±4.00 for what appears to be the same configuration; if these are different configurations or different numbers of runs, please clarify in the text.
  5. [Section V-E, Fig. 11] The patch-size ablation plots show lines without error bars even though the text says multiple runs were performed; please add error bars or explain why they are omitted.
  6. [Table VI] There is a typo in the header, 'Paramters', which should be 'Parameters'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the SOTA claim is supported by supervised training on public benchmarks and held-out test evaluation, not by a derivation that reduces to its inputs.

full rationale

MambaHSI is an empirical architecture paper. Its central claim (state-of-the-art accuracy on PaviaU, Houston, HanChuan, and HongHu) is supported by supervised training on labeled training pixels and evaluation on held-out test pixels (Eq. 10, Eq. 11, Tables I-IV), not by a derivation that reduces to its own inputs. The SpaMB formulation in Eq. 7 feeds a rasterized pixel sequence into a standard Mamba layer, but the accuracy result is not obtained by fitting the reported metric; the claim that this models long-range spatial interaction is an interpretation of the architecture, not a circular definition. The paper cites prior works by the same group (SSFCN [3] and FullyContNet [66]) as related work and as a comparison baseline, but neither citation is load-bearing: FullyContNet's numbers are independently reproduced in the comparison tables, and no parameter is fitted to the reported metric and renamed a prediction. No uniqueness theorem from the authors is invoked to force the design, and no known empirical pattern is merely renamed. The residual concern about the raster-scan spatial Mamba without positional encoding is a correctness or interpretability risk, not a circularity. Therefore no significant circularity is present.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central empirical result rests on standard supervised learning plus the Mamba black box. No new physical entities are introduced. The hand-chosen hyperparameters are the spectral group count G=4 and embedding dimension D=128, both tuned on PaviaU, and the encoder depth is left unspecified. The main hidden modeling assumption is that a raster-scan 1D ordering of pixels is sufficient for Mamba to capture 2D spatial dependencies.

free parameters (4)
  • Spectral group number G = 4
    Chosen via a hyperparameter sweep on PaviaU (Fig. 12a). The spectral Mamba block's sequence length is G, so this directly controls what spectral relations are modeled. G=1 degrades performance; larger values are stable but not better.
  • Embedding dimension D = 128
    Chosen via a hyperparameter sweep on PaviaU (Fig. 12b). Model capacity and FLOPs scale with D; performance peaks near 128 and declines at 512, attributed to overfitting with limited labeled samples.
  • Encoder depth (number of blocks) = not stated
    The paper never specifies how many encoder blocks are stacked. This affects the reported 0.412M parameters and 39.18 GFLOPs and is needed for exact reproduction.
  • Fusion weights w_spa and w_spe = learned during training, values not reported
    Eq. (9) combines spatial and spectral branch outputs using two scalars that are randomly initialized and updated by backpropagation. The final fusion behavior is data-dependent.
assumptions (4)
  • standard math The ZOH discretization of SSMs (Eq. 2) and the selective Mamba layer from Gu and Dao [24] work as described.
    The paper borrows the Mamba block as a black box and does not re-derive it; this is accepted background from the cited literature.
  • domain assumption Spectral bands of hyperspectral images are ordered and approximately contiguous, so treating the spectrum as a sequence is meaningful.
    Stated in Section IV-A: 'hyperspectral images are characterized by approximately contiguous spectral information.' This motivates the spectral Mamba block.
  • ad hoc to paper Flattening the whole 2D image into a raster-scan 1D sequence preserves enough spatial structure for Mamba to learn long-range pixel dependencies.
    Introduced in Section IV-C, Eq. (7). No positional encoding, scan-direction strategy, or 2D inductive bias is discussed or tested.
  • domain assumption Thirty randomly chosen labeled pixels per class plus a separate ten-pixel validation set provide a fair estimate of generalization.
    Training protocol in Section V-A. This is standard in HSI literature but creates class-imbalance and scene-specific splits that may not generalize across sensors.

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Cite this review

Pith. "Pith review of MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification." pith.science (2026). https://pith.science/paper/DFAEO3DJ

@misc{pith2026250104944,
  author       = {Pith},
  title        = {Pith review of: MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DFAEO3DJ}},
  note         = {Machine review of arXiv:2501.04944}
}
read the original abstract

Transformer has been extensively explored for hyperspectral image (HSI) classification. However, transformer poses challenges in terms of speed and memory usage because of its quadratic computational complexity. Recently, the Mamba model has emerged as a promising approach, which has strong long-distance modeling capabilities while maintaining a linear computational complexity. However, representing the HSI is challenging for the Mamba due to the requirement for an integrated spatial and spectral understanding. To remedy these drawbacks, we propose a novel HSI classification model based on a Mamba model, named MambaHSI, which can simultaneously model long-range interaction of the whole image and integrate spatial and spectral information in an adaptive manner. Specifically, we design a spatial Mamba block (SpaMB) to model the long-range interaction of the whole image at the pixel-level. Then, we propose a spectral Mamba block (SpeMB) to split the spectral vector into multiple groups, mine the relations across different spectral groups, and extract spectral features. Finally, we propose a spatial-spectral fusion module (SSFM) to adaptively integrate spatial and spectral features of a HSI. To our best knowledge, this is the first image-level HSI classification model based on the Mamba. We conduct extensive experiments on four diverse HSI datasets. The results demonstrate the effectiveness and superiority of the proposed model for HSI classification. This reveals the great potential of Mamba to be the next-generation backbone for HSI models. Codes are available at https://github.com/li-yapeng/MambaHSI .

Figures

Figures reproduced from arXiv: 2501.04944 by the authors.

Figure 1
Figure 1. Motivation illustration. The local characteristics of CNNs and the quadratic complexity of Transformers limit their ability to achieve fine-grained global modeling. In contrast, the proposed MambaHSI model can achieve pixel-level fine-grained spatial feature modeling with linear complexity. By incorporating spectral sequence information, MambaHSI enhances the extraction of spectral features. observations of the Eart… view at source ↗
Figure 2
Figure 2. Overview of the proposed MambaHSI framework. The whole hyperspectral image is fed into the embedding layer to obtain pixel￾level embeddings. Then these embeddings are taken as the inputs of the encoder to model the long-range dependencies and capture the discriminative features. Finally, the segmentation head classifies the features extracted by the encoder to obtain the final prediction. The encoder block contains … view at source ↗
Figure 3
Figure 3. Pavia Univeristy data set. (a) False color image. (b) Ground truth. (c) Category and sample settings. and implementation details. Then we comprehensively com￾pare with state-of-the-arts methods both qualitatively and quantitatively. Finally, we conduct a detailed ablation study to analyze the effect of the proposed modules. A. Experimental Setup Datasets. To more comprehensively evaluate the effectiveness of the pro… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Houston data set. (a) False color image. (b) Ground truth. (c) Category and sample settings. Nano-Hyperspec imaging sensor equipped on a Leica Aibot X6 UAV V1 platform. The study area is a rural-urban fringe zone with buildings, water, and cultivated land, which contai…
Figure 6
Figure 6. Figure 6: HongHu data set. (a) False color image. (b) Ground truth. (c) Category and sample settings. • GSC-ViT[TGRS2024] [12]: The model adopts a groupwise separable convolution ViT to capture local and global spectral-spatial information for HSI classification. Implement Detai…
Figure 7
Figure 7. Figure 7: Qualitative visualization of the classification map for Pavia University dataset. (a) Ground-truth map. (b) SVM. (c) RF. (d) DMSGer. (e) GiGCN. (f) FullyContNet. (g) CLOLN. (h) Spectralformer. (i) GSC-ViT. (j) The proposed MambaHSI. The meaning of colors refers to [PI…
Figure 8
Figure 8. Figure 8: Qualitative visualization of the classification map for Houston dataset. (a) Ground-truth map. (b) SVM. (c) RF. (d) DMSGer. (e) GiGCN. (f) FullyContNet. (g) CLOLN. (h) Spectralformer. (i) GSC-ViT. (j) The proposed MambaHSI. The meaning of colors refers to [PITH_FULL_I…
Figure 9
Figure 9. Figure 9: Qualitative visualization of the classification map for HanChuan dataset. (a) Ground-truth map. (b) SVM. (c) RF. (d) DMSGer. (e) GiGCN. (f) FullyContNet. (g) CLOLN. (h) Spectralformer. (i) GSC-ViT. (j) The proposed MambaHSI. The meaning of colors refers to [PITH_FULL_…
Figure 10
Figure 10. Figure 10: Qualitative visualization of the classification map for HongHu dataset. (a) Ground-truth map. (b) SVM. (c) RF. (d) DMSGer. (e) GiGCN. (f) FullyContNet. (g) CLOLN. (h) Spectralformer. (i) GSC-ViT. (j) The proposed MambaHSI. The meaning of colors refers to [PITH_FULL_I…
Figure 11
Figure 11. Figure 11: Effect of replacing pixel-wise spatial features with patch features of different sizes on the Pavia University dataset. icantly slower than image-level methods when testing on full images. Additionally, our method achieved the fastest testing and training times among …
Figure 12
Figure 12. Figure 12: Hyper-parameter analysis on the Pavia University dataset. (a) the effect of group number G, (b) the effect of embedding dimension D. The figure reports the mean and standard deviation of the results from five runs. stable. From the results in [PITH_FULL_IMAGE:figures…

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.