Transformers Meet Hyperspectral Imaging: A Comprehensive Study of Models, Challenges and Open Problems

Guyang Zhang; Waleed Abdulla

REVIEW 4 major objections 6 minor 300 references

Transformers Meet Hyperspectral Imaging: A Comprehensive Study of Models, Challenges and Open Problems

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that the scattered literature on Transformer-based hyperspectral image classification is actually one design space, and presents the first end-to-end survey that maps more than 300 papers onto a seven-stage pipeline…

desk verdict A useful, well-organized survey of Transformer-based HSI classification with a pipeline-stage taxonomy, but the 'first dedicated survey' claim rests on a search protocol that is not fully reproducible. read the letter →

arxiv 2506.08596 v1 pith:DITIITMI submitted 2025-06-10 cs.CV cs.AI

Guyang Zhang , Waleed Abdulla This is my paper

classification cs.CVcs.AI

keywords hyperspectralimagingTransformerclassificationsurveyself-attentionspatial-spectralfeatureextractiondeeplearningremotesensing

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 to be the first end-to-end survey devoted to Transformer-based hyperspectral image (HSI) classification, built from a review of more than 300 papers published through 2024. Its central organizing assertion is that the many HSI-Transformer models differ along a single pipeline — preprocessing, tokenization, positional encoding, spatial-spectral feature extraction, self-attention variants, skip connections, and loss design — and that each design choice is best understood against HSI's spatial-spectral character: hundreds of correlated bands, non-adjacent bands with long-range dependencies, scarce labels, and heavy computational cost. The authors argue that the Transformer is a natural fit for the spectral dimension because of those long-range dependencies, but that naive adoption of vision-Transformer recipes fails on HSI's local spatial structure and limited samples. If the survey's organization is right, a researcher entering the field can see which components have been tried, which combinations remain open, and which obstacles — public datasets, on-edge deployment, robustness to illumination and sensor shift, and interpretable attention — are the real bottlenecks.

What carries the argument

The load-bearing device is the seven-stage pipeline taxonomy, a decomposition of any Transformer-based HSI classifier into preprocessing, tokenization, positional encoding, spatial-spectral feature extraction, multi-head self-attention modification, skip connections, and loss design. The taxonomy carries the argument by converting a scattered literature into a grid: each cell is a family of design choices, and the survey places reviewed papers into cells while contrasting the alternatives with HSI-specific constraints, such as grouped spectral tokenization for correlated adjacent bands, Gaussian positional weights for spatial locality, and attention-cost reductions that exploit the spectral dimension. The organizing assumption underneath is that the Transformer's long-range attention matches the long-range dependency of non-adjacent spectral bands, which makes the architecture a principled, not incidental, choice for HSI classification.

What would settle it

Re-running the paper's own search protocol would settle the completeness claim: execute the two stated keyword combinations across IEEE, ScienceDirect, SCOPUS, MDPI, Springer, Wiley, Taylor & Francis, and Sage, inspect the returned titles for Transformer-based hyperspectral image classification, and count how many eligible papers are missing from the survey's reviewed set; a substantial gap, or the existence of an earlier end-to-end survey of the same scope, would falsify the paper's claim to be the first complete survey of the field.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that Transformer-based HSI classification, despite its fragmented literature, is a single design space: methods differ along seven pipeline stages, and the differences are responses to the properties of hyperspectral data. The survey sorts methods by how the image cube is preprocessed (PCA, band selection, autoencoders), how it is split into tokens (pixel-wise, patch-wise, image-based, superpixel clustering), how tokens are embedded (linear, grouped-spectral, convolutional, 3D), how position is encoded (sine-cosine, learnable, relative-bias, Gaussian, or not at all), how spatial and spectral features are extracted (jointly, separately, cascaded, or in parallel branches), how self-attention is modified (alternative QKV generation, in-attention fusion, cost reduction), how layers are skip-connected (dense, mid-range, multilayer), and how losses are designed (cross-entropy variants, label smoothing, focal, joint losses). The authors assert that no prior review covered Transformer-based HSI classification end to end, and that mapping design choices onto HSI's spatial-spectral properties is what makes the taxonomy useful for selecting, combining, or extending components.

Load-bearing premise

The survey's claims of firstness and completeness rest on the assumption that the keyword searches 'Transformer + hyperspectral + classification' and 'self-attention + hyperspectral + classification' across the eight listed databases, together with the exclusion of works that did not add sufficiently novel modifications, captured the full population of relevant Transformer-based hyperspectral image classification papers published through December 2024.

Editorial extensions

If this is right

A researcher entering HSI classification can use the taxonomy as a menu: it shows which pipeline stages are settled, which are contested, and which combinations of tokenization, attention, and loss have not yet been tried.
The survey's open-problems list implies that the field's next gains will come less from new attention variants than from new data, particularly public datasets beyond Indian Pines, Pavia, Salinas, and Houston, and from lightweight models that can run on portable and UAV-borne systems.
Because each design choice is framed as a response to HSI's spatial-spectral properties, the survey implies that stage choices interact: for instance, grouped spectral tokenization changes what positional encoding and attention masking should look like, so copying vision-Transformer defaults stage by stage is likely suboptimal.
For the scarce-label problem that dominates HSI, the catalogued remedies — transfer learning, masked autoencoders, contrastive learning, active and semi-supervised schemes — give practitioners a comparative view of what has been tried, which is a precondition for deciding what to try next.

Reading between the lines

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

A natural extension the paper does not make: the taxonomy could become a reporting checklist, so that future papers state their choices on the seven stages explicitly; if adopted, that would let researchers compare papers without re-implementation.
The survey's qualitative contrasts invite a quantitative test it does not perform: a controlled ablation that fixes the dataset and varies one stage at a time across the standard benchmarks would show which stages actually drive accuracy, rather than which stages appear most often in the literature.
The paper's call for interpretable attention points to a validation criterion it leaves implicit: checking whether learned attention weights align with known spectral absorption or reflection signatures of target materials would test whether the model is learning physics or merely dataset statistics.
Because the completeness claim depends on a subjective novelty filter, the 'more than 300 papers' count is best treated as a lower bound on a fast-moving literature; the same taxonomy could be re-run periodically to track how the design space grows.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's my take on Zhang and Abdulla's survey.

What the paper actually does well: it organizes a fast-growing and messy literature around a concrete design-space taxonomy: preprocessing, tokenization, positional encoding, spectral/spatial feature extraction, self-attention variants, skip connections, loss functions. For someone entering Transformer-based HSI classification, this is a genuinely useful map. The coverage is broad and the authors have clearly read a lot of papers. The '>300' figure is plausible given the reference list. The discussion of challenges (limited labels, high dimensionality, quadratic attention) is accurate, and the future directions are sensible.

The soft spot is the central novelty claim. The abstract says this is "the first end-to-end survey dedicated to Transformer-based HSI classification." The search protocol described in Section 1 doesn't support that negative claim: only two keyword combinations, eight databases, no PRISMA-style flow, no deduplication or exclusion list, and no search for 'survey' or 'review' terms. The authors also exclude venues like arXiv and CVPR/NeurIPS that are major sources of this work. The reference list itself contains non-classification HSI Transformer papers (denoising, super-resolution, unmixing) that Section 1 says are excluded, so the '>300 papers' count isn't cleanly tied to the population. The paper even cites [446], which reportedly reviews 'all algorithms for HSI classification from conventional methods to the most advanced Transformer and Mamba,' without showing how that survey differs. This doesn't sink the paper—a good taxonomy is valuable regardless of priority—but the 'first' claim needs to be softened or backed by a reproducible search.

There's no quantitative meta-analysis, and the exclusion criteria are subjective, but that's typical for a survey. The prose is mostly clear, though there are a few typos and one confusing phrase in the morphFormer description. These are trivial.

Bottom line: this is a solid survey that people in HSI classification will use as a reference. I'd send it to peer review and ask for a reproducible methodology and a more careful 'first' claim. If the authors fix that, I'd cite it.

Referee Report

4 major / 6 minor

Summary. This manuscript presents a survey of Transformer-based hyperspectral image (HSI) classification. The authors organize roughly 300 cited papers into a pipeline-level taxonomy covering learning techniques for scarce labels, preprocessing, patch/pixel tokenization, positional encoding, spatial-spectral feature extraction, multi-head self-attention variants, skip connections, and loss functions. They claim to review more than 300 papers published up to 2025 and to present the first end-to-end survey dedicated to Transformer-based HSI classification. The manuscript also compiles a table of public datasets and proposes future research directions including dataset collection, lightweight models, robustness, and explainability. The survey is descriptive; it reports no quantitative comparisons of the reviewed methods.

Significance. The main contribution is organizational: the paper provides a broad, internally consistent taxonomy of design choices for Transformer-based HSI classifiers, and it cites a large and fragmented literature. If the coverage is reliable, the survey will be a useful entry point for researchers. The dataset table (Table 2) and the discussion of preprocessing, tokenization, and self-attention variants are practical assets. The paper does not contain machine-checked proofs or reproducible code, and it does not perform quantitative meta-analysis. The two claims that need verification are the 'more than 300 papers' count and the 'first dedicated survey' novelty; both are asserted rather than demonstrated.

major comments (4)

[Section 1, 'To generalize current research methods...'] The search methodology is not described at a reproducible level. Only two keyword combinations are given ('Transformer + hyperspectral + classification' and 'self-attention + hyperspectral + classification'), with no query syntax, database-specific filtering, deduplication, or PRISMA-style flow diagram. The exclusion criterion ('works that did not contribute sufficiently novel modifications to HSI classification or Transformer architectures or did not use self-attention mechanisms') is subjective and appears to be applied without a documented protocol. As a result, the claim of reviewing 'more than 300 papers' cannot be independently checked, and the completeness of the corpus for the 'first end-to-end survey' claim is unverified.
[Section 1 vs. Appendix A / Table 1] The stated scope contradicts the included corpus. Section 1 says target detection [55,68,126], anomaly detection [127,150,322], change detection [56,223,91], denoising [69,73,124], unmixing [70,224,246], super-resolution [218,226,252], and reconstruction will 'not be included in this article.' Yet references to such works appear in the bibliography and some are listed in Table 1 (e.g., [101] oil spill mapping, [122] cholangiocarcinoma segmentation, [292] pathology image segmentation, [253] object tracking). This makes the population of 'reviewed papers' ill-defined and prevents a reader from mapping the '>300 papers' figure to the claimed domain of HSI classification.
[Section 1, 'To the best of our knowledge...'] The first-survey claim is a negative claim that the described search cannot support. The keyword strings omit survey/review terms, and the venue list omits arXiv, Google Scholar, and the main computer vision proceedings (CVPR, ICCV, ECCV, NeurIPS), which are major sources of HSI Transformer papers. The manuscript itself cites [446] as a review of HSI classification 'from conventional methods to the most advanced Transformer and Mamba' but never explains why that work is not a dedicated Transformer survey. The authors should either carry out a systematic prior-art search with explicit inclusion criteria or reformulate the novelty claim in a way that is documented and verifiable.
[Section 6.3 and Section 10] The survey is purely descriptive and provides no quantitative summary of the reviewed methods: no accuracy ranges, parameter counts, FLOPs, or training-sample statistics are tabulated. Statements such as 'the above architectures can improve classification performance according to the reviewed works' experiments' (Section 6.3) and the research agenda in Section 10 are therefore not supported by aggregable evidence. Adding a comparative table (e.g., datasets, number of samples, accuracies, model size) or, failing that, explicitly limiting the claims to qualitative observations would make the survey's guidance claims verifiable.

minor comments (6)

[Abstract and Section 1] The abstract states 'published up to 2025' while Section 1 states papers are collected 'until December 2024'; please harmonize the coverage window.
[Section 7.2, near Eq. (18)] The phrase 'enhance the spectral feature representation by by calculating' contains a duplicated word.
[Section 6.3] In the description of morphFormer, 'erotic images are acquired by selecting the pixel with the minimum value' should read 'eroded images are acquired by selecting the pixel with the minimum value.'
[Sections 5.2 and 5.3] There are several typos: 'Gaussian Positional Embendding' should be 'Embedding', and 'dipicted' should be 'depicted.'
[Table 2] Several dataset entries are unstable or non-archival URLs (e.g., the Kaggle share link for the MHSI Choledoch Dataset and the Google Drive link for the oil spill benchmark); prefer DOIs or institutional repository links.
[References] Reference [323] (metasurface-empowered snapshot hyperspectral imaging with convex/deep small-data learning) is cited in the Introduction as an example of a Transformer-based HSI method, but the cited work does not appear to be a Transformer-based classification method; please verify the citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey categorizes external published work and derives no result from its own inputs.

full rationale

This is a literature survey, not a derivation. It reports, categorizes, and taxonomizes externally published Transformer-based HSI classification methods, and it fits no parameters, solves no equations, and generates no predictions from the papers it reviews. The central claim is that the authors reviewed more than 300 papers and provide the first end-to-end survey dedicated to Transformer-based HSI classification; that claim rests on the completeness and interpretation of the literature search, which is an empirical and verifiable matter, not a circularity. Even the weakest assumption identified in the reader's take — that the keyword-based search and exclusion criteria may miss relevant works or earlier surveys — is a correctness and completeness risk, not a circular-reasoning step. The paper also explicitly lists existing surveys and reviews, and notes one work [446] reviewing HSI classification from conventional methods to Transformer and Mamba; this could undermine the novelty claim if [446] is earlier and dedicated, but again that is an external factual question, not a circular reduction. No self-citation is load-bearing: the authors do not invoke their own prior work to define the taxonomy, justify a uniqueness theorem, or smuggle in an ansatz. The survey is self-contained as a literature-organizing document, so the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is a review paper, so there are no fitted parameters or new entities. The survey's validity rests on the accuracy of its characterizations of cited papers and on the completeness of its literature search, both of which are assumed rather than demonstrated.

assumptions (3)

domain assumption The brief descriptions of cited methods accurately reflect the original papers.
The survey categorizes each method into a pipeline stage based on its reading of the cited work; if any reading is wrong, the taxonomy misleads readers. This is not independently checked by the survey.
domain assumption The search strategy and filtering criteria identify the population of relevant Transformer-based HSI classification papers.
The paper states keywords, databases, and an exclusion criterion in Section 1 but does not provide exact queries, search dates per database, or a reproducible screening protocol.
domain assumption No prior review dedicated specifically to Transformer-based HSI classification exists.
The authors assert this in Section 1 based on their search; the claim is load-bearing for the survey's stated novelty but is not verified against a systematic protocol.

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Cite this review

Pith. "Pith review of Transformers Meet Hyperspectral Imaging: A Comprehensive Study of Models, Challenges and Open Problems." pith.science (2026). https://pith.science/paper/DITIITMI

@misc{pith2026250608596,
  author       = {Pith},
  title        = {Pith review of: Transformers Meet Hyperspectral Imaging: A Comprehensive Study of Models, Challenges and Open Problems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DITIITMI}},
  note         = {Machine review of arXiv:2506.08596}
}

read the original abstract

Transformers have become the architecture of choice for learning long-range dependencies, yet their adoption in hyperspectral imaging (HSI) is still emerging. We reviewed more than 300 papers published up to 2025 and present the first end-to-end survey dedicated to Transformer-based HSI classification. The study categorizes every stage of a typical pipeline-pre-processing, patch or pixel tokenization, positional encoding, spatial-spectral feature extraction, multi-head self-attention variants, skip connections, and loss design-and contrasts alternative design choices with the unique spatial-spectral properties of HSI. We map the field's progress against persistent obstacles: scarce labeled data, extreme spectral dimensionality, computational overhead, and limited model explainability. Finally, we outline a research agenda prioritizing valuable public data sets, lightweight on-edge models, illumination and sensor shifts robustness, and intrinsically interpretable attention mechanisms. Our goal is to guide researchers in selecting, combining, or extending Transformer components that are truly fit for purpose for next-generation HSI applications.

Figures

Figures reproduced from arXiv: 2506.08596 by the authors.

**Figure 2.** An overview of the framework of SpectralFormer [43]. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** The overall framework for SSFTT [12]. PCA is first applied to reduce the spectral dimension of the HSI hypercube. After PCA dimension reduction, the HSI data is divided into 3D patches, which are processed by (a) subsequent 3D convolution and 2D convolution for spectral-spatial feature extraction. The extracted features are then flattened and tokenized by (b) Gaussian Weighted Feature Tokenizer, where the features a… view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: An illustration of different feature extraction architectures: (a) cascaded Transformer blocks; (b) parallel CNN [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

**Figure 5.** Figure 5: The framework of DATN [440] is illustrated as an example of feature extraction in sequential/cascaded architectures. The HSI cube X ∈ R H×W×C , where H, W, and C represent the spatial height, width, and spectral bands, respectively, is partitioned into 3D patches of si…

**Figure 6.** Figure 6: The overview framework of CTMixer [59] is depicted as an example of parallel CNN and Transformer branches. The HSI hypercube X ∈ R H×W×C , where H, W, and C represent the spatial height, width, and spectral bands, respectively, is divided into N patches. The HSI patche…

**Figure 7.** Figure 7: The overall framework of morphFormer [64] is illustrated as an example of multiple parallel modules and subsequent cascaded Transformer architectures. The HSI hypercube X ∈ R H×W×C , where H, W, and C represent the spatial height, width, and spectral bands, respectivel…

**Figure 8.** Figure 8: The scaled multi-head self-attention [1]. [PITH_FULL_IMAGE:figures/full_fig_p023_8.png]

**Figure 9.** Figure 9: An illustration of the self-attention module of network: (a) MATA [ [PITH_FULL_IMAGE:figures/full_fig_p025_9.png]

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Bowen Niu, Quanlong Feng, Boan Chen, Cong Ou, Yiming Liu, and Jianyu Yang. Hsi-transunet: A trans- former based semantic segmentation model for crop mapping from uav hyperspectral imagery.Computers and Electronics in Agriculture, 201:107297, 2022

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Jiaqi Yang, Bo Du, and Liangpei Zhang. From center to surrounding: An interactive learning framework for hyperspectral image classification.ISPRS Journal of Photogrammetry and Remote Sensing, 197:145–166, 2023

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Pixel-wise mechanical damage detection of waxy maize using spectral–spatial feature extraction and hyperspectral image.Computers and Electronics in Agriculture, 209:107853, 2023

Fengshuang Liu, Jun Fu, and Rongqiang Zhao. Pixel-wise mechanical damage detection of waxy maize using spectral–spatial feature extraction and hyperspectral image.Computers and Electronics in Agriculture, 209:107853, 2023

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Multi-scale receptive fields: Graph attention neural network for hyperspectral image classification.Expert Systems with Applications, 223:119858, 2023

Yao Ding, Zhili Zhang, Xiaofeng Zhao, Danfeng Hong, Wei Cai, Nengjun Yang, and Bei Wang. Multi-scale receptive fields: Graph attention neural network for hyperspectral image classification.Expert Systems with Applications, 223:119858, 2023

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2023
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Dster: A dense spectral transformer for re- mote sensing spectral super-resolution.International Journal of Applied Earth Observation and Geoinformation, 109:102773, 2022

Jiang He, Qiangqiang Yuan, Jie Li, Yi Xiao, Xinxin Liu, and Yun Zou. Dster: A dense spectral transformer for re- mote sensing spectral super-resolution.International Journal of Applied Earth Observation and Geoinformation, 109:102773, 2022

2022
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A spectrum contextual self-attention deep learning network for hyperspectral inversion of soil metals.Ecological Indicators, 152:110351, 2023

Tingyu Zhang, Quan Fu, Runqing Tian, Yang Zhang, and Zenghui Sun. A spectrum contextual self-attention deep learning network for hyperspectral inversion of soil metals.Ecological Indicators, 152:110351, 2023

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Learning from small data for hyperspectral image classifica- tion.Signal Processing, 213:109183, 2023

Xiaoyan Luo, Sen Li, Xiaofeng Shi, and Jihao Yin. Learning from small data for hyperspectral image classifica- tion.Signal Processing, 213:109183, 2023

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2022
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Development of deep learning methodology for maize seed variety recognition based on improved swin transformer.Agronomy, 12(8), 2022

Chunguang Bi, Nan Hu, Yiqiang Zou, Shuo Zhang, Suzhen Xu, and Helong Yu. Development of deep learning methodology for maize seed variety recognition based on improved swin transformer.Agronomy, 12(8), 2022

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Spectral- former: Rethinking hyperspectral image classification with transformers.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, and Jocelyn Chanussot. Spectral- former: Rethinking hyperspectral image classification with transformers.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

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Ji He, Lina Zhao, Hongwei Yang, Mengmeng Zhang, and Wei Li. Hsi-bert: Hyperspectral image classification using the bidirectional encoder representation from transformers.IEEE Transactions on Geoscience and Remote Sensing, 58(1):165–178, Jan 2020

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Stransfuse: Fusing swin transformer and convolutional neural network for remote sensing image semantic segmentation

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Chunhui Zhao, Boao Qin, Shou Feng, Wenxiang Zhu, Lifu Zhang, and Jinchang Ren. An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 6...

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Xin He and Yushi Chen. Optimized input for cnn-based hyperspectral image classification using spatial transformer network.IEEE Geoscience and Remote Sensing Letters, 16(12):1884–1888, Dec 2019

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Siamese transformer network for hyperspectral image target detection.IEEE Transactions on Geoscience and Remote Sensing, 60:1–19, 2022

Weiqiang Rao, Lianru Gao, Ying Qu, Xu Sun, Bing Zhang, and Jocelyn Chanussot. Siamese transformer network for hyperspectral image target detection.IEEE Transactions on Geoscience and Remote Sensing, 60:1–19, 2022

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Zilong Zhong, Ying Li, Lingfei Ma, Jonathan Li, and Wei-Shi Zheng. Spectral–spatial transformer network for hyperspectral image classification: A factorized architecture search framework.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

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Deep hierarchical vision transformer for hyperspectral and lidar data classification.IEEE Transactions on Image Processing, 31:3095– 3110, 2022

Zhixiang Xue, Xiong Tan, Xuchu Yu, Bing Liu, Anzhu Yu, and Pengqiang Zhang. Deep hierarchical vision transformer for hyperspectral and lidar data classification.IEEE Transactions on Image Processing, 31:3095– 3110, 2022

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Convolutional transformer network for hyperspectral image classification.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

Zhengang Zhao, Dan Hu, Hao Wang, and Xianchuan Yu. Convolutional transformer network for hyperspectral image classification.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

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Zhaohui Xue, Qi Xu, and Mengxue Zhang. Local transformer with spatial partition restore for hyperspectral image classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:4307–4325, 2022

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Shangdong Zheng, Zebin Wu, Yang Xu, Zhihui Wei, and Antonio Plaza. Learning orientation information from frequency-domain for oriented object detection in remote sensing images.IEEE Transactions on Geoscience and Remote Sensing, 60:1–12, 2022

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Yanheng Wang, Danfeng Hong, Jianjun Sha, Lianru Gao, Lian Liu, Yonggang Zhang, and Xianhui Rong. Spectral–spatial–temporal transformers for hyperspectral image change detection.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

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Hyperspectral image classification using group-aware hierarchical transformer.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

Shaohui Mei, Chao Song, Mingyang Ma, and Fulin Xu. Hyperspectral image classification using group-aware hierarchical transformer.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022. 35 APREPRINT- SEPTEMBER22, 2025

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Ruoxi Song, Yining Feng, Wei Cheng, Zhenhua Mu, and Xianghai Wang. Bs2t: Bottleneck spatial–spectral transformer for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–17, 2022

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Haoyang Yu, Zhen Xu, Ke Zheng, Danfeng Hong, Hao Yang, and Meiping Song. Mstnet: A multilevel spectral–spatial transformer network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–13, 2022

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Kai Liu, Weiwei Sun, Yijun Shao, Weiwei Liu, Gang Yang, Xiangchao Meng, Jiangtao Peng, Dehua Mao, and Kai Ren. Mapping coastal wetlands using transformer in transformer deep network on china zy1-02d hyperspectral satellite images.IEEE Journal of Selected Topics in Applied Eart...

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Leyuan Fang, Yifan Jiang, Yinglong Yan, Jun Yue, and Yue Deng. Hyperspectral image instance segmentation using spectral–spatial feature pyramid network.IEEE Transactions on Geoscience and Remote Sensing, 61:1–13, 2023

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Jing Bai, Zheng Wen, Zhu Xiao, Fawang Ye, Yongdong Zhu, Mamoun Alazab, and Licheng Jiao. Hyperspectral image classification based on multibranch attention transformer networks.IEEE Transactions on Geoscience and Remote Sensing, 60:1–17, 2022

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Swalpa Kumar Roy, Ankur Deria, Chiranjibi Shah, Juan M. Haut, Qian Du, and Antonio Plaza. Spectral–spatial morphological attention transformer for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–15, 2023

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Fractional fourier image transformer for multimodal remote sensing data classification.IEEE Transactions on Neural Networks and Learning Systems, pages 1–13, 2022

Xudong Zhao, Mengmeng Zhang, Ran Tao, Wei Li, Wenzhi Liao, Lianfang Tian, and Wilfried Philips. Fractional fourier image transformer for multimodal remote sensing data classification.IEEE Transactions on Neural Networks and Learning Systems, pages 1–13, 2022

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Jiaqi Zou, Wei He, and Hongyan Zhang. Lessformer: Local-enhanced spectral-spatial transformer for hyperspec- tral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–16, 2022

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Shang-Qi Deng, Liang-Jian Deng, Xiao Wu, Ran Ran, Danfeng Hong, and Gemine Vivone. Psrt: Pyramid shuffle-and-reshuffle transformer for multispectral and hyperspectral image fusion.IEEE Transactions on Geoscience and Remote Sensing, 61:1–15, 2023

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Hongyu Chen, Guangyi Yang, and Hongyan Zhang. Hider: A hyperspectral image denoising transformer with spatial–spectral constraints for hybrid noise removal.IEEE Transactions on Neural Networks and Learning Systems, pages 1–15, 2022

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Preetam Ghosh, Swalpa Kumar Roy, Bikram Koirala, Behnood Rasti, and Paul Scheunders. Hyperspectral unmixing using transformer network.IEEE Transactions on Geoscience and Remote Sensing, 60:1–16, 2022

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Swalpa Kumar Roy, Ankur Deria, Danfeng Hong, Behnood Rasti, Antonio Plaza, and Jocelyn Chanussot. Multimodal fusion transformer for remote sensing image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–20, 2023

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Translution-snet: A semisupervised hyperspectral image stripe noise removal based on transformer and cnn.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

Chengjun Wang, Miaozhong Xu, Yonghua Jiang, Guo Zhang, Hao Cui, Litao Li, and Da Li. Translution-snet: A semisupervised hyperspectral image stripe noise removal based on transformer and cnn.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

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Masked auto-encoding spec- tral–spatial transformer for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

Damian Ibañez, Ruben Fernandez-Beltran, Filiberto Pla, and Naoto Yokoya. Masked auto-encoding spec- tral–spatial transformer for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

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Yunhao Gao, Mengmeng Zhang, Junjie Wang, and Wei Li. Cross-scale mixing attention for multisource remote sensing data fusion and classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–15, 2023

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Grafting transformer on automatically designed convolutional neural network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–16, 2022

Xizhe Xue, Haokui Zhang, Bei Fang, Zongwen Bai, and Ying Li. Grafting transformer on automatically designed convolutional neural network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–16, 2022

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Bing Tu, Xiaolong Liao, Qianming Li, Yishu Peng, and Antonio Plaza. Local semantic feature aggregation-based transformer for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

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Yishu Peng, Yuwen Zhang, Bing Tu, Qianming Li, and Wujing Li. Spatial–spectral transformer with cross- attention for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

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Tftn: A transformer-based fusion tracking framework of hyperspectral and rgb.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

Chunhui Zhao, Hongjiao Liu, Nan Su, and Yiming Yan. Tftn: A transformer-based fusion tracking framework of hyperspectral and rgb.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

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Hsi-mixer: Hyperspectral image classification using the spectral–spatial mixer representation from convolutions.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

Hongbo Liang, Wenxing Bao, Xiangfei Shen, and Xiaowu Zhang. Hsi-mixer: Hyperspectral image classification using the spectral–spatial mixer representation from convolutions.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

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Hyperspectral image classification based on graph transformer network and graph attention mechanism.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

Xiaofeng Zhao, Jiahui Niu, Chuntong Liu, Yao Ding, and Danfeng Hong. Hyperspectral image classification based on graph transformer network and graph attention mechanism.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

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Fengfeng Wang, Jie Li, Qiangqiang Yuan, and Liangpei Zhang. Local–global feature-aware transformer based residual network for hyperspectral image denoising.IEEE Transactions on Geoscience and Remote Sensing, 60:1–19, 2022

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Microscopic hyperspectral image classification based on fusion transformer with parallel cnn.IEEE Journal of Biomedical and Health Informatics, 27(6):2910–2921, June 2023

Weijia Zeng, Wei Li, Mengmeng Zhang, Hao Wang, Meng Lv, Yue Yang, and Ran Tao. Microscopic hyperspectral image classification based on fusion transformer with parallel cnn.IEEE Journal of Biomedical and Health Informatics, 27(6):2910–2921, June 2023

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A model-inspired approach with transformers for hyperspectral pansharpening.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:7187–7202, 2022

Yanli Shang, Jianjun Liu, Jinlong Yang, and Zebin Wu. A model-inspired approach with transformers for hyperspectral pansharpening.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:7187–7202, 2022

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A spectral–spatial fusion transformer network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–16, 2023

Diling Liao, Cuiping Shi, and Liguo Wang. A spectral–spatial fusion transformer network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–16, 2023

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Ning Huyan, Xiangrong Zhang, Dou Quan, Jocelyn Chanussot, and Licheng Jiao. Aud-net: A unified deep de- tector for multiple hyperspectral image anomaly detection via relation and few-shot learning.IEEE Transactions on Neural Networks and Learning Systems, pages 1–15, 2022

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Jigang Ding, Xiaorun Li, and Liaoying Zhao. Cdformer: A hyperspectral image change detection method based on transformer encoders.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

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Csit: A multiscale vision transformer for hyperspectral image classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:9266–9277, 2022

Wenxuan He, Weiliang Huang, Shuhong Liao, Zhen Xu, and Jingwen Yan. Csit: A multiscale vision transformer for hyperspectral image classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:9266–9277, 2022

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Feng Zhao, Shijie Li, Junjie Zhang, and Hanqiang Liu. Convolution transformer fusion splicing network for hyperspectral image classification.IEEE Geoscience and Remote Sensing Letters, 20:1–5, 2023

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Ping Tang, Meng Zhang, Zhihui Liu, and Rong Song. Double attention transformer for hyperspectral image classification.IEEE Geoscience and Remote Sensing Letters, 20:1–5, 2023

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Yifan Sun, Bing Liu, Xuchu Yu, Anzhu Yu, Pengqiang Zhang, and Zhixiang Xue. Exploiting discriminative advantage of spectrum for hyperspectral image classification: Spectralformer enhanced by spectrum motion feature.IEEE Geoscience and Remote Sensing Letters, 20:1–5, 2023. 37 A...

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Jing Yao, Bing Zhang, Chenyu Li, Danfeng Hong, and Jocelyn Chanussot. Extended vision transformer (exvit) for land use and land cover classification: A multimodal deep learning framework.IEEE Transactions on Geoscience and Remote Sensing, 61:1–15, 2023

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Yuwen Zhang, Yishu Peng, Bing Tu, and Yaru Liu. Local information interaction transformer for hyperspectral and lidar data classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16:1130–1143, 2023

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Yu Fang, Qiaolin Ye, Le Sun, Yuhui Zheng, and Zebin Wu. Multiattention joint convolution feature representation with lightweight transformer for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–14, 2023

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Zhixiang Xue, Xuchu Yu, Anzhu Yu, Bing Liu, Pengqiang Zhang, and Shentong Wu. Self-supervised feature learning for multimodal remote sensing image land cover classification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

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Two-branch pure transformer for hyperspectral image classification.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

Xin He, Yushi Chen, and Qingyun Li. Two-branch pure transformer for hyperspectral image classification.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

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Xuming Zhang, Yuanchao Su, Lianru Gao, Lorenzo Bruzzone, Xingfa Gu, and Qingjiu Tian. A lightweight transformer network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–17, 2023

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A transformer-based three- branch siamese network for hyperspectral object tracking

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Di Wang, Jing Zhang, Bo Du, Liangpei Zhang, and Dacheng Tao. Dcn-t: Dual context network with transformer for hyperspectral image classification.IEEE Transactions on Image Processing, 32:2536–2551, 2023

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Lamei Liu, Dongyu Zuo, Ying Wang, and Haicheng Qu. Feedback-enhanced few-shot transformer learning for small-sized hyperspectral image classification.IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2022

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Wenchao Qi, Changping Huang, Yibo Wang, Xia Zhang, Weiwei Sun, and Lifu Zhang. Global–local 3-d convolutional transformer network for hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–20, 2023

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Ting Lu, Mengkai Liu, Wei Fu, and Xudong Kang. Grouped multi-attention network for hyperspectral image spectral-spatial classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–12, 2023

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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