Reframing SAR Target Recognition as Visual Reasoning: A Chain-of-Thought Dataset with Multimodal LLMs

Chaoran Li; Siyuan Mu; Xingguo Xu

REVIEW 4 major objections 5 minor 20 references

Reframing SAR Target Recognition as Visual Reasoning: A Chain-of-Thought Dataset with Multimodal LLMs

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper claims that SAR target recognition can be reframed as a multimodal visual-reasoning task, and that GPT-4o, given candidate labels and chain-of-thought prompting, correctly infers the target category for the vast majority of…

desk verdict Useful new SAR reasoning dataset, but the headline accuracy and coherence claims rest on a retry-until-correct loop that has not been properly reported. read the letter →

arxiv 2507.09535 v1 pith:JHUEP45Y submitted 2025-07-13 eess.SP

Chaoran Li , Xingguo Xu , Siyuan Mu This is my paper · ORCID

classification eess.SP

keywords SyntheticApertureRadartargetrecognitionmultimodallargelanguagemodelschain-of-thoughtreasoningFAIR-CSARdatasetvisualGPT-4ointerpretability

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 tries to establish that Synthetic Aperture Radar target recognition can be reformulated as a visual reasoning task for multimodal large language models, rather than only a feature-classification problem. It builds a new dataset from FAIR-CSAR in which each target image is paired with candidate categories and a GPT-4o-generated chain of thought, and reports that the model correctly infers the target category for most instances, with about 1.68% of targets resisting all reasoning attempts. A sympathetic reader should care because, if true, MLLMs could act as interpretable decision-support tools in SAR analysis and could produce training data for more specialized recognition models.

What carries the argument

The central object is a new reasoning dataset built from FAIR-CSAR: for each target, a cropped SAR image with one red bounding-box overlay, a three-choice candidate list containing the ground truth plus two visually or semantically similar distractors, and a GPT-generated chain-of-thought text of roughly 110 to 200 words that ends in a predicted label. The argument runs through a retry loop: any wrong prediction is sent back to GPT with altered candidates and temperature 1.2, up to three rounds, and only chains that converge to the ground truth are retained. That loop is what converts raw model outputs into a dataset of validated reasoning chains.

What would settle it

Run GPT-4o exactly once per target on the same crops with fixed candidates and a single temperature, then compare that single-pass accuracy with the paper's pipeline result; if most of the 1.68% failure-rate margin disappears, the retry loop manufactured the apparent capability. A second check is to ask SAR radar experts to score whether the retained chains cite radar-specific evidence such as scattering or geometry rather than generic plausibility, which would settle whether the chains are genuine reasoning or post hoc rationalization.

Watch

Extended reading notes

Core claim

The paper's central claim is that a multimodal large language model, given a SAR image with the target marked by a red rectangle and a short list of candidate categories that includes the true label, can classify that target through explicit chain-of-thought reasoning and produce interpretable explanations. Using GPT-4o on a sampled subset of FAIR-CSAR, the authors find that after up to three re-inference rounds the model reaches the ground-truth label for the overwhelming majority of targets; the 1.68% failure rate is concentrated in visually similar aircraft pairs, especially Airbus A220 misidentified as Airbus A320. The paper presents this as evidence that SAR recognition can be treated as a reasoning task and that MLLM-generated chains are usable as a dataset resource.

Load-bearing premise

The load-bearing premise is that matching the FAIR-CSAR ground-truth label counts as correct recognition and that GPT's retained chain of thought is a faithful, interpretable record of its reasoning, even though wrong answers are re-prompted up to three times with modified candidates and only correct chains are kept.

Editorial extensions

If this is right

A SAR analyst or automated pipeline could present top-k candidate labels to an MLLM and receive a target label plus an interpretable explanation of the inference.
The 1.68% hard-error rate defines a concrete boundary around current MLLM capability, mainly for weak-feature or ambiguous targets such as visually similar aircraft.
The dataset can be used to fine-tune smaller, specialized multimodal models for SAR reasoning, reducing dependence on closed commercial APIs.
The failure analysis identifies category pairs with high SAR visual similarity, which could guide feature-based recognizers to incorporate context or uncertainty rather than relying on pixel templates alone.

Reading between the lines

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

The reported success rate is a property of the pipeline with up to three re-prompting attempts, not a single-pass accuracy; a fair comparison with classical SAR classifiers would run the model once per target.
Matching the FAIR-CSAR ground-truth label does not guarantee that the retained chain is the model's actual reasoning, so the chains may be post hoc rationalizations until radar-domain experts verify them.
Because the red rectangle reveals the target location, the task is anchored recognition rather than detection; extending the setup to unmarked scenes would test whether scene context alone can drive inference.
The prompt explicitly instructs GPT not to reason about why the target is not a certain category, which may suppress a legitimate elimination strategy and alter both accuracy and chain content for visually similar distractors.

Share X Bluesky LinkedIn Reddit HN

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

The genuinely new thing here is the artifact: a SAR-specific visual reasoning dataset built on FAIR-CSAR, with cropped targets, candidate label sets, and GPT-4o generated chain-of-thought traces. I am not aware of a prior SAR dataset with CoT annotations, and the authors are right that this is a gap. The task formulation is sensible, and the data format description is concrete enough to reproduce. There is also a useful failure analysis: the Airbus A220/A320 confusion pattern and the Tower Crane/Tank correction behavior are the kind of observations that could inform future work.

What does not hold up is the central capability claim. Section 3.3 describes a retry-and-select protocol: every wrong answer is sent back up to three times with changed candidate categories and temperature 1.2, and only the chains that end at the ground-truth label are kept. So the 1.68% hard-case rate is not a single-pass error rate; it is the complement of the probability that at least one of up to four attempts lands on the correct label. With three candidates, a uniform guesser would pass roughly 80% of the time, so the reported 98.32% success tells you almost nothing about the model's SAR recognition ability. The paper never reports per-attempt accuracy or the distribution of attempts before success. The coherence claim is equally unmeasured: no human evaluation, no inter-annotator agreement, and Section 5 explicitly defers expert verification of logical validity to future work. The abstract's wording ("capable of generating logically coherent and interpretable inferences") goes beyond what the evidence supports.

To be fair, the paper itself flags part of the problem. Section 4.3 admits that GPT often produces the correct label only once across repeated trials while otherwise repeating the same erroneous path, which is precisely the selection bias that undermines the headline. And Section 5 lists expert verification as future work. So the authors are not hiding the procedure, but they are not drawing the right conclusion from it.

The dataset could still be valuable if released with the exact prompts and per-attempt records. As written, a no-retry baseline and an external rating of reasoning quality are required before the feasibility claim can be accepted.

This paper deserves peer review, not a desk rejection. I would send it out with a request for major revision: report single-pass accuracy, report the retry distribution, add a no-retry baseline, and either add expert ratings or soften the claims to match the evidence.

Referee Report

4 major / 5 minor

Summary. This paper proposes treating SAR target recognition as a chain-of-thought reasoning task for multimodal LLMs. It constructs a dataset from a subset of FAIR-CSAR by cropping each target, overlaying a red bounding box, supplying three candidate categories (including the ground-truth label), and asking GPT-4o to produce a final category and an explanatory reasoning chain. Section 3.3 describes an iterative re-inference protocol: misclassified targets are re-prompted up to three times with temperature 1.2 and modified candidate sets, and only chains ending at the ground-truth label are retained. The paper reports a 1.68% 'hard-to-classify' rate after this procedure and claims that MLLMs generate logically coherent, interpretable inferences, with failure-case analysis identifying Airbus A220 as the most frequent error category. The authors also defer domain-expert validation of the reasoning chains to future work.

Significance. If the claims were supported, the contribution would be useful: a SAR visual-reasoning dataset with candidate labels and reasoning traces could support interpretability studies and decision-support prototypes, and the failure-case analysis is a helpful starting point. The paper is also honest about the re-prompting protocol and about cases where the model reaches the correct label only once across repeated trials. However, the reported statistics do not currently support the central capability claim, because the headline success rate is produced by an acceptance-sampling loop, and the coherence of the retained chains is not independently assessed. No code, data-release link, total sample sizes, or exact prompt templates are provided, so the dataset contribution is not yet reproducible. With single-pass accuracy, per-attempt analysis, and expert evaluation, the claims could be made testable and the paper could be a useful dataset/analysis contribution.

major comments (4)

[Section 3.3 and Section 4.2] The 98.32% 'correct' rate and 1.68% hard-case rate are properties of the retry-and-select pipeline, not of GPT-4o's recognition capability. Section 3.3 states that every misclassified instance is re-prompted with modified candidates and temperature 1.2 up to three times, and only the attempt that matches the ground truth is retained. With three candidate categories, a model that guesses uniformly at random would pass this filter with probability 1 - (2/3)^4 ≈ 0.802 over the initial attempt plus three retries, so the reported outcome cannot distinguish genuine SAR recognition from selection by repeated draws. The paper does not report single-pass accuracy, per-attempt accuracy, the distribution of the number of attempts needed, or the exact candidate-modification rule. The claim in Section 4.2 that GPT is able to 'correctly infer the target category for the majority of instances' should be replaced by a no-re-prompting evaluation, or explicitly reframed as a property of the data-generation process.
[Section 4.3 and Section 5] The assertion that the chains are 'logically coherent and interpretable inferences' is unsupported. The reasoning chains are generated by the same model that is being evaluated and are filtered by whether they end at the ground-truth label; no human evaluation, inter-annotator agreement, or comparison with a post-hoc-explanation baseline is reported. Section 5 explicitly defers domain-expert verification of logical validity and technical accuracy to future work. Moreover, Section 4.3 itself notes that GPT 'may occasionally produce the correct label once, but in aggregate, it tends to generate incorrect predictions more frequently, often following the same erroneous reasoning path.' This indicates that the retained successful chains may be atypical samples rather than representative reasoning traces. Because the dataset's value as training or evaluation data depends on the faithfulness of the chains, the authors should release all attempts (including rejected ones), and report an expert rating study with a defined scoring rubric.
[Section 3.1 and Section 4.1] The paper's main deliverable is a dataset, but it does not state the total number of SAR images, the total number of target instances, the per-category sample sizes (only figures/percentages are shown), or the number of instances excluded because they belong to the 'Other Aircraft' and 'Other Ships' categories. There is also no release URL, DOI, exact prompt template, or code for the GPT interaction and candidate-generation procedure. Without these, the dataset cannot be used or the results reproduced. Please add a data-availability section with the sample sizes, exclusion counts, and the full prompt and re-inference protocol.
[Section 4.2 and Section 3.1] Matching the FAIR-CSAR ground-truth subclass label is treated as the definition of correct recognition, but the paper itself reports that Airbus A220 and Airbus A320 are visually very similar and that A220 is frequently misidentified as A320. If exact subclass labels are not visually recoverable from a cropped SAR patch, then even a perfect single-pass score could be an artifact of label ambiguity rather than evidence of reasoning. At minimum, the authors should quantify the chance-level performance of the candidate guessing baseline, report human expert accuracy on the same cropped patches, and provide an analysis of label ambiguity (e.g., inter-subclass feature distance) for the classes included in the dataset.

minor comments (5)

[Title] The title contains a typo: 'T ARGET' should be 'TARGET'.
[Section 2.3] The text contains 'SV AMP'; this should be 'SVAMP' (the dataset used in reference [20]).
[Section 3.3 and Section 5] GPT-4o is described as an open-source model, but GPT-4o is a proprietary API model; 'API-accessible' would be accurate.
[Page 1 footnote and Section 5] The footnote 'This work is in progress' should be resolved before publication; the final manuscript should state whether the reported results are preliminary or final.
[References] References [8] and [15] are the same survey with different arXiv numbers; please deduplicate.

Circularity Check

1 steps flagged · score 6.0 of 10

Retry-and-select protocol makes the reported 98.32% success rate a property of the acceptance filter, so the MLLM capability claim is partially circular.

fitted input called prediction [Sections 3.2, 3.3, and 4.2]
"Next, considering that a large model without access to candidate labels might produce categories that cannot be directly compared with the ground truth, we provide a set of candidate classes for each target, including its true category. ... Incorrect predictions can arise due to several factors, including targets being too small or partially occluded in the image. To obtain a reliable reasoning chain for each target, any misclassified instance is reprocessed and sent back to GPT for re-inference."

The reported 98.32% success rate is not a single-pass measurement: every wrong answer is retried up to three additional times with the ground truth always present among the candidate labels, and all chains that do not end at the ground-truth label are discarded. The 1.68% 'hard-to-classify' figure is therefore the residual of an acceptance-sampling filter, not the model's intrinsic error rate. With three candidates, even uniform random guessing would pass this filter with probability about 1 - (2/3)^4, roughly 80%, so the statistic cannot distinguish genuine SAR recognition from retry-fuelled selection.

full rationale

The paper is transparent about its retry protocol, and the dataset itself may be a useful resource, but the headline inference about MLLM capability reduces to the selection rule. Section 3.3 keeps only the correct result and its chain; Section 4.2 then cites the post-retry residual as evidence that GPT can infer target categories in most scenarios. This is a clear case of a fitted input being called a prediction: the reported accuracy is a property of the acceptance filter, not of the model's unaided reasoning. The coherence claim is similarly based only on retained chains, and Section 4.3 admits that GPT may produce the correct label only once while repeatedly generating an erroneous path, suggesting the kept chains are atypical. Section 5 further defers domain-expert verification of logical validity and technical accuracy to future work. No self-citation chain is load-bearing, and the paper does not cite its own prior work to force conclusions; the circularity is instead in the evaluation protocol. Because the central feasibility claim is partially an artifact of the retry-and-select procedure, a score of 6 is appropriate: one or more headline quantities reduce by construction, though the dataset construction itself is not circular.

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

No new physical entities or mediators are introduced. The main add-on is the SAR-CoT dataset artifact itself, which is not released and thus cannot be inspected. The numerical free parameters are experimental design choices that directly shape the reported success rate.

free parameters (4)

Number of candidate categories = 3
Each inference task provides exactly one correct and two distractor labels, chosen by the authors; accuracy is therefore a 3-way forced-choice result and not comparable to open-set recognition.
Maximum retry rounds = 3
Targets are re-prompted up to three times until the model produces the ground-truth label; this protocol largely determines the reported 1.68% failure rate.
GPT-4o temperature for re-inference = 1.2
Temperature is raised to 1.2 in retries to vary outputs; this is an ad hoc sampling choice that affects the measured success rate.
Excluded ambiguous categories = Other Aircraft, Other Ships removed
Instances without precise semantic definitions are dropped because correctness cannot be assessed; this changes the class distribution and removes the most ambiguous cases.

assumptions (4)

domain assumption FAIR-CSAR ground-truth labels are correct and complete for the sampled targets.
The paper uses these labels as the oracle for judging GPT outputs in Sections 3.3 and 4.2, with no independent verification effort.
domain assumption The cropped single-target image with a red overlay retains the information needed for recognition.
Section 3.2 feeds the model exactly one cropped target; if bounding boxes are noisy or the overlay hides features, the reasoning task is not measuring SAR recognition ability.
ad hoc to paper GPT-generated chain-of-thought text faithfully reflects the model's decision process.
The paper treats CoT as interpretable inference in Sections 3.3 and 5, but does not verify chains against radar physics or human expert reasoning; LLM CoT can be post hoc rationalization.
domain assumption The randomly sampled subset of FAIR-CSAR is representative of the full dataset.
Section 3.1 says a random subset is used, but the sample size and seed are not reported, and the class imbalance in Figure 3 may not match the original benchmark.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Reframing SAR Target Recognition as Visual Reasoning: A Chain-of-Thought Dataset with Multimodal LLMs." pith.science (2026). https://pith.science/paper/JHUEP45Y

@misc{pith2026250709535,
  author       = {Pith},
  title        = {Pith review of: Reframing SAR Target Recognition as Visual Reasoning: A Chain-of-Thought Dataset with Multimodal LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JHUEP45Y}},
  note         = {Machine review of arXiv:2507.09535}
}

read the original abstract

In the context of Synthetic Aperture Radar (SAR) image recognition, traditional methods often struggle with the intrinsic limitations of SAR data, such as weak texture, high noise, and ambiguous object boundaries. This work explores a novel perspective by reformulating SAR target recognition as a multimodal reasoning task. We leverage multimodal large language models (MLLMs), specifically GPT-4o, to perform target classification based on SAR imagery, guided by candidate categories and enhanced with Chain-of-Thought (CoT) reasoning. A new dataset is constructed based on the FAIR-CSAR benchmark, comprising raw SAR images, structured target annotations, candidate label sets, and GPT-generated CoT reasoning chains. Experimental results show that the MLLMs are capable of generating logically coherent and interpretable inferences in most scenarios. Our analysis highlights both the strengths and current limitations of MLLMs in interpreting SAR imagery, and we provide detailed insights into model behavior through failure case analysis. This work demonstrates the feasibility of incorporating MLLMs into SAR analysis pipelines and establishes a foundation for future research in SAR-oriented visual reasoning.

Figures

Figures reproduced from arXiv: 2507.09535 by the authors.

**Figure 2.** Data format example in the dataset 4 Dataset Analysis and Statistics 4.1 Target Categories and Inference Texts A statistical analysis was conducted on the types and quantities of targets present in the SAR images used in this study. The results are shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Number of instances in each category [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Distribution of reasoning error samples 6 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

20 extracted references · 16 canonical work pages

[1]

A tutorial on synthetic aperture radar

Alberto Moreira, Gerhard Krieger, Irena Hajnsek, Konstantinos P Papathanassiou, and Chenguang Hu. A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine , 1(1):6–43, 2013

work page 2013
[2]

A comprehensive survey on sar atr in deep-learning era

Jianwei Li, Zhentao Yu, Lu Yu, Pu Cheng, Jie Chen, and Cheng Chi. A comprehensive survey on sar atr in deep-learning era. Remote Sensing, 13(3):531, 2021

work page 2021
[3]

Towards sar automatic target recognition: Multi-category sar image classification based on light weight vision transformer

Guibin Zhao, Pengfei Li, Zhibo Zhang, Fusen Guo, Xueting Huang, Wei Xu, Jinyin Wang, and Jianlong Chen. Towards sar automatic target recognition: Multi-category sar image classification based on light weight vision transformer. Remote Sensing, 15(5):1336, 2023

work page 2023
[4]

Recent advances in target recognition with synthetic aperture radar based on machine learning: A review

Jinlong Geng and Lei Ma. Recent advances in target recognition with synthetic aperture radar based on machine learning: A review. IEEE Geoscience and Remote Sensing Magazine , 9(1):53–78, 2021

work page 2021
[5]

Recent advances in deep-learning-based sar image target detection and recognition

Feng Gao, Wei Zhang, Ming Li, Yan Wang, and Xiaodong Chen. Recent advances in deep-learning-based sar image target detection and recognition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99):1–31, 2025. License: CC BY 4.0

work page 2025
[6]

Quality assessment of sar-to-optical image translation

Chenfang Liu, Yuli Sun, Yanjie Xu, Zhongzhen Sun, Xianghui Zhang, Lin Lei, and Gangyao Kuang. Quality assessment of sar-to-optical image translation. Remote Sensing, 12(21):3472, 2024

work page 2024
[7]

Deep learning meets sar: A review of recent advances

Lingjuan Zhu, Feng Xu, and Ya-Qiu Jin. Deep learning meets sar: A review of recent advances. ISPRS Journal of Photogrammetry and Remote Sensing , 179:215–231, 2021

work page 2021
[8]

Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Philip S. Yu. Multimodal large language models: A survey. arXiv preprint arXiv:2402.01988, 2024

work page Pith review arXiv 2024

Show all 20 references

[9]

Gpt-4 technical report

OpenAI. Gpt-4 technical report. https://openai.com/research/gpt-4v-system-card , 2024. Accessed July 2025

2024
[10]

Gemini 1: Unlocking multimodal reasoning

Google DeepMind. Gemini 1: Unlocking multimodal reasoning. https://deepmind.google/technologies/ gemini/, 2023. Accessed: July 2025

2023
[11]

Sar image target recognition based on local binary pattern features and sparse representation classification

Bo Gao, Zhiguo Huang, and Bo Yu. Sar image target recognition based on local binary pattern features and sparse representation classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13:2134–2144, 2020

2020
[12]

A survey of deep learning-based sar image target recognition methods

Xiaofeng Wang, Jun Zhang, and Qi Liu. A survey of deep learning-based sar image target recognition methods. Remote Sensing, 15(2):366, 2023. 7 Reframing SAR Target Recognition as Visual Reasoning: A Chain-of-Thought Dataset with Multimodal LLMs

2023
[13]

Towards sar automatic target recognition: Multi-category sar image classification based on lightweight vision transformer

Guibin Zhao, Pengfei Li, Zhibo Zhang, et al. Towards sar automatic target recognition: Multi-category sar image classification based on lightweight vision transformer. IEEE Transactions on Geoscience and Remote Sensing , 2023

2023
[14]

FAIR-CSAR: A benchmark dataset for fine-grained object detection and recognition based on single-look complex SAR images

Youming Wu, Yuxi Suo, Qingbiao Meng, Wei Dai, Tian Miao, Wenchao Zhao, Zhiyuan Yan, Wenhui Diao, Guocun Xie, Qingyang Ke, Yiming Zhao, Kun Fu, and Xian Sun. FAIR-CSAR: A benchmark dataset for fine-grained object detection and recognition based on single-look complex SAR images...

2024
[15]

Multimodal large language models: A survey

Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Philip S Yu. Multimodal large language models: A survey. arXiv preprint arXiv:2305.06580, 2023

2023 arXiv
[16]

Flamingo: a visual language model for few-shot learning

Jean-Baptiste Alayrac et al. Flamingo: a visual language model for few-shot learning. arXiv preprint arXiv:2204.14198, 2022

2022 arXiv
[17]

Remote sensing visual question answering via multimodal large language models

Yuan Li, Ke Zhang, Yiran Zhou, et al. Remote sensing visual question answering via multimodal large language models. Remote Sensing, 16(1):122, 2024

2024
[18]

Chain of thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903, 2022

2022 arXiv
[19]

Training verifiers to solve math word problems

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, John Schulman, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021

arXiv
[20]

Are nlp models really able to solve simple math word problems? arXiv preprint arXiv:2103.07191, 2021

Harsh Patel, Mudit Jain, and Ankur P Singh. Are nlp models really able to solve simple math word problems? arXiv preprint arXiv:2103.07191, 2021. 8

2021 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

A tutorial on synthetic aperture radar

Alberto Moreira, Gerhard Krieger, Irena Hajnsek, Konstantinos P Papathanassiou, and Chenguang Hu. A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine , 1(1):6–43, 2013

work page 2013

[2] [2]

A comprehensive survey on sar atr in deep-learning era

Jianwei Li, Zhentao Yu, Lu Yu, Pu Cheng, Jie Chen, and Cheng Chi. A comprehensive survey on sar atr in deep-learning era. Remote Sensing, 13(3):531, 2021

work page 2021

[3] [3]

Towards sar automatic target recognition: Multi-category sar image classification based on light weight vision transformer

Guibin Zhao, Pengfei Li, Zhibo Zhang, Fusen Guo, Xueting Huang, Wei Xu, Jinyin Wang, and Jianlong Chen. Towards sar automatic target recognition: Multi-category sar image classification based on light weight vision transformer. Remote Sensing, 15(5):1336, 2023

work page 2023

[4] [4]

Recent advances in target recognition with synthetic aperture radar based on machine learning: A review

Jinlong Geng and Lei Ma. Recent advances in target recognition with synthetic aperture radar based on machine learning: A review. IEEE Geoscience and Remote Sensing Magazine , 9(1):53–78, 2021

work page 2021

[5] [5]

Recent advances in deep-learning-based sar image target detection and recognition

Feng Gao, Wei Zhang, Ming Li, Yan Wang, and Xiaodong Chen. Recent advances in deep-learning-based sar image target detection and recognition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99):1–31, 2025. License: CC BY 4.0

work page 2025

[6] [6]

Quality assessment of sar-to-optical image translation

Chenfang Liu, Yuli Sun, Yanjie Xu, Zhongzhen Sun, Xianghui Zhang, Lin Lei, and Gangyao Kuang. Quality assessment of sar-to-optical image translation. Remote Sensing, 12(21):3472, 2024

work page 2024

[7] [7]

Deep learning meets sar: A review of recent advances

Lingjuan Zhu, Feng Xu, and Ya-Qiu Jin. Deep learning meets sar: A review of recent advances. ISPRS Journal of Photogrammetry and Remote Sensing , 179:215–231, 2021

work page 2021

[8] [8]

Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Philip S. Yu. Multimodal large language models: A survey. arXiv preprint arXiv:2402.01988, 2024

work page Pith review arXiv 2024