Paper Citation Record · LEDGER

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2509.00835.

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

pith.paper-citation-record.v1
2509.00835 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:16:47.122962Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

45 of 45 outbound references displayed

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verified fuzzy39
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f10e189b-4c3d-4209-9a2c-c67a38d5dfba · outbound

This paper cites an unresolved cited work.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:16:47.628428Z

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Observation 4621663f-be1a-44d9-8f45-ad1726ad0f93 · outbound

This paper cites an unresolved cited work.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:16:47.619918Z

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Observation f30b60f2-90ba-4fbf-a95d-378cd8ed5ec3 · outbound

This paper cites A.; Loveland, T.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss A.; Loveland, T

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.610739Z

Source-reported events for the cited work

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Observation 45fb5dc4-08e3-472e-aeb8-2772892d1e16 · outbound

This paper cites GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.601624Z

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Observation 05acd7e2-b4d2-4d36-81e8-6fb72786c9a6 · outbound

This paper cites J.; Khan, H.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss J.; Khan, H

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.592474Z

Source-reported events for the cited work

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Observation c1c2a615-0d80-4232-930a-df3fb4db91c3 · outbound

This paper cites Hyperspectral and multispectral data fusion: A comparative review of the recent literature.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Hyperspectral and multispectral data fusion: A comparative review of the recent literature

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.583748Z

Source-reported events for the cited work

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Observation 29773322-1262-43b0-8952-a4561b8020fc · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Remote sensing image scene classification: Benchmark and state of the art

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.574969Z

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Observation c05f5e56-4d13-4e1b-9406-32838d17d34e · outbound

This paper cites A survey on deep learning-based change detection from high-resolution remote sensing images.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss A survey on deep learning-based change detection from high-resolution remote sensing images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.565703Z

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Observation 9d190267-e10e-45b1-b20c-86598c9a2929 · outbound

This paper cites Trends and prospects of techniques for haze removal from degraded images: A survey.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Trends and prospects of techniques for haze removal from degraded images: A survey

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.489094Z

Source-reported events for the cited work

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Observation a0d95161-9c64-438f-a5df-4b616404eb62 · outbound

This paper cites A review of remote sensing image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss A review of remote sensing image dehazing

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.479567Z

Source-reported events for the cited work

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Observation f861a7ae-68c5-4c7f-805f-c30891d5f211 · outbound

This paper cites an unresolved cited work.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Unresolved cited work

Reference 11

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Source-reported events for the cited work

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Observation 7d7711dc-0258-4c48-a2b9-fbddb0c8d8d3 · outbound

This paper cites G.; Clayton, C.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss G.; Clayton, C

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.460877Z

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Observation 78baa757-57ca-49df-bb43-d87522461b73 · outbound

This paper cites C.; Montes, M.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss C.; Montes, M

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.451499Z

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Observation f6c17b42-ec46-49b0-9a1e-19375d613d17 · outbound

This paper cites an unresolved cited work.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:16:47.442856Z

Source-reported events for the cited work

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Observation fb6bec30-9946-4bc5-8ea2-ff56c408f52f · outbound

This paper cites K.; Vasikarla, S.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss K.; Vasikarla, S

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.433885Z

Source-reported events for the cited work

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Observation b6676c32-94f6-4086-9bb6-a22812651f8d · outbound

This paper cites H.; Chen, L.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss H.; Chen, L

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.424765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation edcfda0c-e41b-4821-a065-5cdd2851d0a2 · outbound

This paper cites Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.415483Z

Source-reported events for the cited work

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Observation 76d654d1-91e2-4675-bcfc-e98c35daa6a1 · outbound

This paper cites Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.406254Z

Source-reported events for the cited work

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Observation 2dde263e-66ff-44f0-9934-db1d3e80e94c · outbound

This paper cites Single satellite image dehazing via linear intensity transformation and local property analysis.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Single satellite image dehazing via linear intensity transformation and local property analysis

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.397749Z

Source-reported events for the cited work

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Observation d53a9b44-a0aa-4e73-bc3b-443013753b22 · outbound

This paper cites Q.; Jiang, X.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Q.; Jiang, X

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.388919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4aaacc2-4662-4417-b0cd-a2e475ae6844 · outbound

This paper cites A comprehensive survey and taxonomy on single image dehazing based on deep learning.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss A comprehensive survey and taxonomy on single image dehazing based on deep learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.380502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 20b49804-7e17-464c-89b9-2bf71c14fb13 · outbound

This paper cites Single satellite optical imagery dehazing using SAR image prior based on conditional generative adversarial networks.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Single satellite optical imagery dehazing using SAR image prior based on conditional generative adversarial networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.371431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb24ac3f-72fd-404e-80b0-d9825fec4cc3 · outbound

This paper cites Hybrid high-resolution learning for single remote sensing satellite image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Hybrid high-resolution learning for single remote sensing satellite image dehazing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.362501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87d90a3e-0845-44e3-aa80-2c063539ff3d · outbound

This paper cites Learning an effective transformer for remote sensing satellite image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Learning an effective transformer for remote sensing satellite image dehazing

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.353427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8f3da42-d11e-4773-9f1e-6bd71c2011ea · outbound

This paper cites An efficient multi-scale transformer for satellite image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss An efficient multi-scale transformer for satellite image dehazing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.343439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b773550c-a483-4cda-9a9d-1d10bdc67e52 · outbound

This paper cites SAR-to-optical image translation using SSIM and perceptual loss based cycle-consistent GAN.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss SAR-to-optical image translation using SSIM and perceptual loss based cycle-consistent GAN

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.333219Z

Source-reported events for the cited work

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Observation cb3d9efb-dc5f-4b2a-8fc2-a8f40a11b794 · outbound

This paper cites Cloud-GAN: Cloud removal for Sentinel-2 imagery using cyclic consistent generative adversarial networks.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Cloud-GAN: Cloud removal for Sentinel-2 imagery using cyclic consistent generative adversarial networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.323758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23750cfa-2a02-4126-a0cb-b9ec5c86a4e2 · outbound

This paper cites DehazeNet: An end-to-end system for single image haze removal.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss DehazeNet: An end-to-end system for single image haze removal

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.314266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a831996-8b98-4756-8b25-b512c487bda1 · outbound

This paper cites M2SCN: Multi-model self- correcting network for satellite remote sensing single-image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss M2SCN: Multi-model self- correcting network for satellite remote sensing single-image dehazing

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.305352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e34ec23-6468-47ce-81d1-54619f679ba7 · outbound

This paper cites A Novel Framework for Satellite Image Dehazing Using Advanced Computational Techniques.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss A Novel Framework for Satellite Image Dehazing Using Advanced Computational Techniques

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.295982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 250749c6-53c0-4d9b-989a-cfea271be6fc · outbound

This paper cites Remote sensing image dehazing using heterogeneous atmospheric light prior.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Remote sensing image dehazing using heterogeneous atmospheric light prior

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.287328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ef8b2b4-bf7c-402f-8ff0-318bbda1127c · outbound

This paper cites IDF- CR: Iterative diffusion process for divide-and-conquer cloud removal in remote-sensing images.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss IDF- CR: Iterative diffusion process for divide-and-conquer cloud removal in remote-sensing images

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.278476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 16569737-dc68-498f-83ac-f68c8f7e0ef9 · outbound

This paper cites SCANet: Self-paced semi-curricular attention network for non-homogeneous image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss SCANet: Self-paced semi-curricular attention network for non-homogeneous image dehazing

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.267488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0901c11c-1cb6-46df-ad89-e2f24bdba68b · outbound

This paper cites Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network

Reference 34

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Source-reported events for the cited work

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Observation 994cf740-c049-4c77-b9b7-248b1e64c4ff · outbound

This paper cites Vision transformers for single image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Vision transformers for single image dehazing

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.256652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2251037a-1cb7-4f26-8aad-dcd4e5128fea · outbound

This paper cites Information Fusion.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Information Fusion

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.246853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e78d171d-a876-4fb0-b6ad-4c8fccc9e0e6 · outbound

This paper cites Remote Sensing Image Dehazing via Dual-View Knowledge Transfer.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Remote Sensing Image Dehazing via Dual-View Knowledge Transfer

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.236756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c6281141-057c-44f6-9bd9-076692d964e7 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Swin transformer: Hierarchical vision transformer using shifted windows

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.227663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2ab9d7a2-c5e1-4d34-98c9-e805b37ee4bc · outbound

This paper cites Efficient dehazing method for outdoor and remote sensing images.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Efficient dehazing method for outdoor and remote sensing images

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.218085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fe3bf5e2-d9d2-4bf8-a585-4a5a104aaae4 · outbound

This paper cites A Dehazing Method for UAV Remote Sensing Based on Global and Local Feature Collaboration.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss A Dehazing Method for UAV Remote Sensing Based on Global and Local Feature Collaboration

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.207655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9210b503-ac74-48a6-8cdc-5f4e9f9b281f · outbound

This paper cites U-Shaped Dual Attention Vision Mamba Network for Satellite Remote Sensing Single-Image Dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss U-Shaped Dual Attention Vision Mamba Network for Satellite Remote Sensing Single-Image Dehazing

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.197878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 97e3def9-c687-4a86-8940-fc70258be572 · outbound

This paper cites ICL-Net: Inverse cognitive learning network for remote sensing image dehazing.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss ICL-Net: Inverse cognitive learning network for remote sensing image dehazing

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.187563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f42f06a-a4b5-498f-a01d-e4fcb5f7a7f1 · outbound

This paper cites Guided image filtering.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Guided image filtering

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.178146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 39f9097e-0bf0-4fd0-b9c4-5269dbe2a119 · outbound

This paper cites A Remote Sensing Image Dataset for Cloud Removal.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss A Remote Sensing Image Dataset for Cloud Removal

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T13:16:47.119570Z

Source-reported events for the cited work

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Observation ac7ff842-a5ad-4f74-aa9f-edc93fb38e5a · outbound

This paper cites Single image haze removal using dark channel prior.

Satellite Image Utilization for Dehazing with Swin Transformer-Hybrid U-Net and Watershed loss Single image haze removal using dark channel prior

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:16:47.167932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.