Paper Citation Record · LEDGER

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2606.28922.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.28922 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T08:27:13.945810Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact2
verified fuzzy23
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7811b82a-da83-4fbd-bd94-34be101fd5b0 · outbound

This paper cites A low-cost polarimetric radar system based on mechanical rotation and its signal processing[J].IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(2): 4744– 4765.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration A low-cost polarimetric radar system based on mechanical rotation and its signal processing[J].IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(2): 4744– 4765

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:57:38.813460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 18735dad-77be-4ce8-ab54-b3e8bbdbde25 · outbound

This paper cites PolSAR ship detection based on superpixel-level contrast en- hancement[J].IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1–5.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration PolSAR ship detection based on superpixel-level contrast en- hancement[J].IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1–5

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.376866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ffcb4f68-4915-4746-a4bc-a25e37e1c825 · outbound

This paper cites Generative adversarial nets[C]//Proceedings of Advances in Neural Information Processing Systems.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Generative adversarial nets[C]//Proceedings of Advances in Neural Information Processing Systems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.401000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40e45780-2f26-4583-9344-304b52395217 · outbound

This paper cites Denoising diffusion probabilistic models[C]//Proceedings of Advances in Neural Information Processing Systems.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Denoising diffusion probabilistic models[C]//Proceedings of Advances in Neural Information Processing Systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:57:38.797477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7aeadd9f-9f7f-4da2-a890-a6c118ddcf66 · outbound

This paper cites Pixel recurrent neural net- works[C]//Proceedings of the 33rd International Conference on Machine Learning.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Pixel recurrent neural net- works[C]//Proceedings of the 33rd International Conference on Machine Learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:57:38.767392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96d53d5c-b403-4a4f-a581-3935e605434d · outbound

This paper cites an unresolved cited work.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-07-10T20:57:38.781700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5b1ab787-c9b6-4fb2-b6bf-b43d13e54db8 · outbound

This paper cites Few-shot class-incremental SAR target recognition via orthogonal distributed features[J].IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(1): 325–341.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Few-shot class-incremental SAR target recognition via orthogonal distributed features[J].IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(1): 325–341

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:34.483100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e547aea6-2102-4eba-9bd7-1667b5833815 · outbound

This paper cites Fast SAR image segmentation with deep task-specific superpixel sampling and soft graph convolution[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1–16.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Fast SAR image segmentation with deep task-specific superpixel sampling and soft graph convolution[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1–16

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:34.485186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3313da12-1819-4340-a1e5-93d55d6981d7 · outbound

This paper cites an unresolved cited work.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-07-10T20:47:34.487055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2e9cee9a-0d55-4b91-9bea-2b2067949127 · outbound

This paper cites an unresolved cited work.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-07-10T20:47:34.472259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 94af8590-f951-434c-a7f6-13db49f85eda · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent ad- versarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Unpaired image-to-image translation using cycle-consistent ad- versarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:34.474610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 844f5e00-168c-4620-a7b0-d07b1fdba56e · outbound

This paper cites Image-to-image translation with conditional adversarial net- works[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Image-to-image translation with conditional adversarial net- works[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:34.476786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2a14494c-a953-4c79-8e2f-c60f29bda08f · outbound

This paper cites Optical-to-SAR image translation via neural partial differential equa- tions[C]//Proceedings of the 31st International Joint Conference on Artificial Intelligence.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Optical-to-SAR image translation via neural partial differential equa- tions[C]//Proceedings of the 31st International Joint Conference on Artificial Intelligence

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:34.478867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 92403a88-9171-4757-879c-9191e214646f · outbound

This paper cites The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:34:27.300272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bffed236-6ed1-41ea-8c8e-972a2a598ab6 · outbound

This paper cites Contrastive learning for unpaired image-to-image transla- tion[C]//Proceedings of Computer Vision–ECCV 2020.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Contrastive learning for unpaired image-to-image transla- tion[C]//Proceedings of Computer Vision–ECCV 2020

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.402841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19629dd9-3e09-4e50-88a7-ee5f5c842485 · outbound

This paper cites StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.399017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 66211080-8080-4802-b687-d789c5bbcdbd · outbound

This paper cites SAR-to-SAR image translation for domain adaptation in SAR ship detection[J].Remote Sensing, 2020, 12(16): 2607.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration SAR-to-SAR image translation for domain adaptation in SAR ship detection[J].Remote Sensing, 2020, 12(16): 2607

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.390858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 50ee61e4-904b-4cad-ae7e-41f55f1589f6 · outbound

This paper cites Asurveyonhypothesisgenerationforsci- entific discovery in the era of large language models.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Asurveyonhypothesisgenerationforsci- entific discovery in the era of large language models

Reference 18

Resolution: metadata mismatch; doi, observed 2026-06-30T08:34:27.294822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 309baf9c-a968-491d-8ed4-a5fce4d31b65 · outbound

This paper cites Selftok: A self-tokenization method for visual-language model pre- training[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Selftok: A self-tokenization method for visual-language model pre- training[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.381608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f6e69e1a-3d65-4d18-8f1f-6ccc2563ce25 · outbound

This paper cites Parallel autoregressive generation[C]//Proceedings of the 41st International Conference on Machine Learning.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Parallel autoregressive generation[C]//Proceedings of the 41st International Conference on Machine Learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.393013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 285f2ebd-89dd-4705-abd9-edb0c4759a45 · outbound

This paper cites SAR image synthesis with diffusion models[C]//Proceedings of 2024 IEEE Radar Conference.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration SAR image synthesis with diffusion models[C]//Proceedings of 2024 IEEE Radar Conference

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.388497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f0f49a78-f91d-4d6f-966e-872a7f95a0bb · outbound

This paper cites DiffuSAR: Frequency domain-aware diffusion model for SAR image generation[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 8202–8215.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration DiffuSAR: Frequency domain-aware diffusion model for SAR image generation[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 8202–8215

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.386581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a65e3e30-8342-4322-99ef-d68047ce50c9 · outbound

This paper cites DiffDet4SAR: Diffusion-based aircraft target detection network for SAR images[J].IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1–5.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration DiffDet4SAR: Diffusion-based aircraft target detection network for SAR images[J].IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1–5

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.395102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 87989e0a-c71b-46a4-8836-271b1b676440 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:34:27.297797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d5098dec-d752-428c-bf04-e8891a94114d · outbound

This paper cites LoRA: Low-rank adaptation of large language mod- els[C]//Proceedings of International Conference on Learning Representations.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration LoRA: Low-rank adaptation of large language mod- els[C]//Proceedings of International Conference on Learning Representations

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.374887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7a0e01a2-0a0f-468f-b5a3-9ecb4519ac8f · outbound

This paper cites MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:34:27.304206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a49c51a1-78a3-4a55-9a39-db25ab793c58 · outbound

This paper cites Flow straight and fast: Learning to generate and reconstruct with rectified flow[C]//Proceedings of the 11th International Conference on Learning Representations.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Flow straight and fast: Learning to generate and reconstruct with rectified flow[C]//Proceedings of the 11th International Conference on Learning Representations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.379159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5250cb7b-b8dd-4647-88e1-94a54f48e383 · outbound

This paper cites Scattering characteristics guided network for ISAR space target component segmentation[J].IEEE Geoscience and Remote Sensing Letters, 2025, 22: 4009505.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Scattering characteristics guided network for ISAR space target component segmentation[J].IEEE Geoscience and Remote Sensing Letters, 2025, 22: 4009505

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:57:38.782424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b75b33f1-3762-4317-95f4-704bdbe75933 · outbound

This paper cites Fast task-specific region merging for SAR image segmentation[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5222316.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Fast task-specific region merging for SAR image segmentation[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5222316

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.384389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bb6a7c08-70e4-4ecc-9382-a94d23e2d5ca · outbound

This paper cites Deep residual learning for image recogni- tion[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration Deep residual learning for image recogni- tion[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:47:35.396970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.