pith. sign in

arxiv: 2601.12052 · v2 · submitted 2026-01-17 · 💻 cs.CV

Task-Driven Prompt Learning: A Joint Framework for Multi-modal Cloud Removal and Segmentation

Zaiyan Zhang , Jie Li , Shaowei Shi , Qiangqiang Yuan This is my paper

Pith reviewed 2026-05-16 13:01 UTC · model grok-4.3

classification 💻 cs.CV
keywords cloud removalremote sensingmulti-modal fusionprompt learningland-cover segmentationSAR integrationtask-driven training
0
0 comments X

The pith

A task-driven prompt framework jointly removes clouds from optical images and improves land-cover segmentation accuracy using only 15% of typical parameters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes TDP-CR, a joint framework for cloud removal and land-cover segmentation in remote sensing imagery. It addresses the mismatch between visually pleasing cloud removal and semantic utility by using a Prompt-Guided Fusion mechanism. This mechanism employs a learnable degradation prompt to encode cloud properties and selectively integrate SAR data where needed. The approach uses a parameter-efficient two-phase training strategy to achieve superior results on the LuojiaSET-OSFCR dataset.

Core claim

The authors claim that by jointly optimizing cloud removal and segmentation with a prompt-guided fusion of optical and SAR data, their TDP-CR framework produces analysis-ready data that outperforms separate or heavy multi-task methods in both reconstruction fidelity and semantic accuracy.

What carries the argument

Prompt-Guided Fusion (PGF) mechanism that combines global channel context with local prompt-conditioned spatial bias to adaptively integrate SAR information based on a learnable degradation prompt encoding cloud thickness and uncertainty.

Load-bearing premise

That the learnable degradation prompt can reliably encode cloud thickness and spatial uncertainty to selectively integrate SAR data without introducing artifacts that degrade segmentation performance.

What would settle it

Running the model on the LuojiaSET-OSFCR test set and finding no improvement in mIoU or introduction of segmentation errors in cloudy regions would falsify the superiority claim.

Figures

Figures reproduced from arXiv: 2601.12052 by Jie Li, Qiangqiang Yuan, Shaowei Shi, Zaiyan Zhang.

Figure 2
Figure 2. Figure 2: Prompt-Guided Fusion (PGF) block combines global channel context [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visual comparison of cloud removal results. TDP-CR preserves fine [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of learned degradation prompt maps. We apply Principal [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visual comparison of land cover segmentation. TDP-CR preserves [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
read the original abstract

Optical remote sensing imagery is indispensable for Earth observation, yet persistent cloud occlusion limits its downstream utility. Most cloud removal (CR) methods are optimized for low-level fidelity and can over-smooth textures and boundaries that are critical for analysis-ready data (ARD), leading to a mismatch between visually plausible restoration and semantic utility. To bridge this gap, we propose TDP-CR, a task-driven multimodal framework that jointly performs cloud removal and land-cover segmentation. Central to our approach is a Prompt-Guided Fusion (PGF) mechanism, which utilizes a learnable degradation prompt to encode cloud thickness and spatial uncertainty. By combining global channel context with local prompt-conditioned spatial bias, PGF adaptively integrates Synthetic Aperture Radar (SAR) information only where optical data is corrupted. We further introduce a parameter-efficient two-phase training strategy that decouples reconstruction and semantic representation learning. Experiments on the LuojiaSET-OSFCR dataset demonstrate the superiority of our framework: TDP-CR surpasses heavy state-of-the-art baselines by 0.18 dB in PSNR while using only 15\% of the parameters, and achieves a 1.4\% improvement in mIoU consistently against multi-task competitors, effectively delivering analysis-ready data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes TDP-CR, a joint task-driven multimodal framework for cloud removal and land-cover segmentation. It centers on a Prompt-Guided Fusion (PGF) mechanism that employs a learnable degradation prompt to encode cloud thickness and spatial uncertainty, adaptively fusing SAR data with optical imagery only where needed. A parameter-efficient two-phase training strategy decouples reconstruction from semantic learning. Experiments on the LuojiaSET-OSFCR dataset report 0.18 dB PSNR gains over heavy baselines using 15% of the parameters and a consistent 1.4% mIoU improvement over multi-task competitors.

Significance. If the reported gains hold under rigorous validation, the work offers a meaningful advance by aligning low-level restoration with downstream semantic utility in remote sensing, rather than optimizing solely for visual fidelity. The prompt-based selective fusion and two-phase training provide a practical route to parameter-efficient multimodal ARD generation, with potential applicability to other degradation-aware vision tasks.

major comments (2)
  1. [Experiments] Experiments section: The central claims of 0.18 dB PSNR and 1.4% mIoU superiority lack ablations that isolate the learnable degradation prompt within PGF from the contributions of two-phase training or standard multimodal fusion. Without these controls, it remains unclear whether the prompt specifically enables artifact-free SAR integration or if gains arise from other design choices.
  2. [Method] Method (PGF description): The claim that the prompt encodes cloud thickness and spatial uncertainty via global channel context plus local prompt-conditioned bias is load-bearing for the selective-fusion argument, yet the manuscript provides no direct validation such as prompt activation maps correlated against cloud-thickness ground truth or quantitative boundary-artifact metrics on land-cover classes.
minor comments (2)
  1. The abstract states gains are 'consistent' against multi-task competitors but does not list the exact baselines, number of runs, or variance; adding these would strengthen the empirical section.
  2. Dataset details for LuojiaSET-OSFCR (e.g., cloud coverage distribution, SAR-optical alignment quality) are referenced but not fully specified, limiting reproducibility assessment.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and outline the revisions we will make to strengthen the experimental validation and methodological justification.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: The central claims of 0.18 dB PSNR and 1.4% mIoU superiority lack ablations that isolate the learnable degradation prompt within PGF from the contributions of two-phase training or standard multimodal fusion. Without these controls, it remains unclear whether the prompt specifically enables artifact-free SAR integration or if gains arise from other design choices.

    Authors: We agree that isolating the prompt's contribution is necessary for rigor. In the revised manuscript we will add a dedicated ablation table that includes: (i) full TDP-CR, (ii) PGF replaced by standard concatenation-based multimodal fusion, (iii) two-phase training removed (end-to-end joint training), and (iv) prompt removed while retaining two-phase training. These controls will quantify the incremental benefit of the learnable degradation prompt on both PSNR and boundary-sensitive mIoU. revision: yes

  2. Referee: [Method] Method (PGF description): The claim that the prompt encodes cloud thickness and spatial uncertainty via global channel context plus local prompt-conditioned bias is load-bearing for the selective-fusion argument, yet the manuscript provides no direct validation such as prompt activation maps correlated against cloud-thickness ground truth or quantitative boundary-artifact metrics on land-cover classes.

    Authors: We will add qualitative prompt activation maps overlaid on input cloud masks to illustrate spatial selectivity. Because the LuojiaSET-OSFCR dataset does not contain explicit cloud-thickness annotations, direct quantitative correlation with thickness ground truth is not possible. To address boundary artifacts we will report additional edge-aware metrics (boundary F1 and perimeter IoU) on the segmentation output, demonstrating that PGF reduces over-smoothing at land-cover transitions compared with baselines. revision: partial

standing simulated objections not resolved
  • Quantitative correlation of prompt activation maps against cloud-thickness ground truth, as no such per-pixel thickness annotations exist in the LuojiaSET-OSFCR dataset.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces TDP-CR as a novel task-driven multimodal framework centered on the Prompt-Guided Fusion (PGF) mechanism with a learnable degradation prompt. Performance claims rest on direct empirical comparisons against baselines on the external LuojiaSET-OSFCR dataset, reporting specific metric gains (0.18 dB PSNR, 1.4% mIoU) without any equations or derivations that reduce by construction to self-defined inputs, fitted parameters renamed as predictions, or load-bearing self-citations. The two-phase training strategy and PGF components are presented as independent design choices validated through ablation-style experiments rather than circular self-reference. No ansatz smuggling, uniqueness theorems from prior self-work, or renaming of known results occurs; the chain is self-contained via standard multimodal fusion adapted to the joint task.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard deep-learning assumptions plus the domain premise that SAR provides usable complementary signal under cloud cover; the learnable prompt parameters are fitted during training.

free parameters (1)
  • learnable degradation prompt parameters
    Prompt weights are optimized on training data to encode cloud thickness and uncertainty.
axioms (1)
  • domain assumption SAR data supplies complementary information usable for optical cloud removal
    Invoked as the basis for selective fusion in the PGF module.

pith-pipeline@v0.9.0 · 5524 in / 1204 out tokens · 42396 ms · 2026-05-16T13:01:55.932650+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

  1. [1]

    Spatial and temporal distribution of clouds observed by modis onboard the terra and aqua satellites,

    M. D. King, S. Platnick, W. P. Menzel, S. A. Ackerman, and P. A. Hubanks, “Spatial and temporal distribution of clouds observed by modis onboard the terra and aqua satellites,”IEEE Trans. Geosci. Remote Sens., vol. 51, no. 7, pp. 3826–3852, 2013

    work page 2013

  2. [2]

    Missing information reconstruction of remote sens- ing data: A technical review,

    H. Shen, X. Li, Q. Cheng, C. Zeng, G. Yang, H. Li, and L. Zhang, “Missing information reconstruction of remote sens- ing data: A technical review,”IEEE Geosci. Remote Sens. Mag., vol. 3, no. 3, pp. 61–85, 2015

    work page 2015

  3. [3]

    Multiscale restoration of missing data in optical time-series images with masked spatial-temporal attention network,

    Z. Zhang, J. Yan, Y . Liang, J. Feng, H. He, and L. Cao, “Multiscale restoration of missing data in optical time-series images with masked spatial-temporal attention network,”IEEE Trans. Geosci. Remote Sens., vol. 63, pp. 1–15, 2025

    work page 2025

  4. [4]

    Simple baselines for image restoration,

    L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” inProc. Eur. Conf. Comput. Vis. (ECCV). Springer, 2022, pp. 17–33

    work page 2022

  5. [5]

    Selective kernel networks,

    X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” inProc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), 2019, pp. 510–519

    work page 2019

  6. [6]

    Cloud removal in sentinel-2 imagery using a deep residual neural network and sar-optical data fusion,

    A. Meraner, P. Ebel, X. X. Zhu, and M. Schmitt, “Cloud removal in sentinel-2 imagery using a deep residual neural network and sar-optical data fusion,”ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 333 – 346, 2020

    work page 2020

  7. [7]

    Glf-cr: Sar-enhanced cloud removal with global–local fusion,

    F. Xu, Y . Shi, P. Ebel, L. Yu, G.-S. Xia, W. Yang, and X. X. Zhu, “Glf-cr: Sar-enhanced cloud removal with global–local fusion,” ISPRS J. Photogramm. Remote Sens., vol. 192, pp. 268–278, 2022

    work page 2022

  8. [8]

    Hpn- cr: Heterogeneous parallel network for sar-optical data fusion cloud removal,

    P. Gu, W. Liu, S. Feng, T. Wei, J. Wang, and H. Chen, “Hpn- cr: Heterogeneous parallel network for sar-optical data fusion cloud removal,”IEEE Trans. Geosci. Remote Sens., 2025

    work page 2025

  9. [9]

    Effective cloud removal for remote sensing images by an improved mean- reverting denoising model with elucidated design space,

    Y . Liu, W. Li, J. Guan, S. Zhou, and Y . Zhang, “Effective cloud removal for remote sensing images by an improved mean- reverting denoising model with elucidated design space,”Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), 2025

    work page 2025

  10. [10]

    Cloudseg: A multi-modal learning framework for robust land cover mapping under cloudy conditions,

    F. Xu, Y . Shi, W. Yang, G.-S. Xia, and X. X. Zhu, “Cloudseg: A multi-modal learning framework for robust land cover mapping under cloudy conditions,”ISPRS J. Photogramm. Remote Sens., vol. 214, pp. 21–32, 2024

    work page 2024

  11. [11]

    Hdrsa-net: Hybrid dynamic residual self-attention network for sar-assisted optical image cloud and shadow removal,

    J. Pan, J. Xu, X. Yu, G. Ye, M. Wang, Y . Chen, and J. Ma, “Hdrsa-net: Hybrid dynamic residual self-attention network for sar-assisted optical image cloud and shadow removal,”ISPRS J. Photogramm. Remote Sens., vol. 218, pp. 258–275, 2024

    work page 2024

  12. [12]

    Segformer: Simple and efficient design for semantic segmentation with transformers,

    E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,”Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 34, pp. 12 077–12 090, 2021

    work page 2021