Pith Number

pith:5DPFWDJ3

pith:2026:5DPFWDJ3ANTIRISMTZSG22QUZ6

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AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors

Lu Li, Su Luo, Wenwen Liu, Xiaoyu Li, Ying Liu, Zuopeng Zhao

AnyBand-Diff reconstructs complete spectral information in remote sensing images from arbitrary band subsets using physics-guided diffusion.

arxiv:2605.14341 v1 · 2026-05-14 · cs.CV

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\pithnumber{5DPFWDJ3ANTIRISMTZSG22QUZ6}

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

AnyBand-Diff achieves accurate spectral reconstruction and generates reliable imagery by integrating a Masked Conditional Diffusion backbone with dual stochastic masking, Physics-Guided Sampling using gradients from a differentiable physical model, and a Multi-Scale Physical Loss.

C2weakest assumption

That the differentiable physical model used for gradient steering accurately represents real-world radiometric and spectral relationships and that its gradients reliably steer the denoising process onto the manifold of physically plausible solutions without introducing new artifacts.

C3one line summary

AnyBand-Diff is a spectral-prior-guided diffusion model that unifies remote sensing image generation and band repair while maintaining radiometric fidelity through physics-guided sampling and multi-scale losses.

References

128 extracted · 128 resolved · 0 Pith anchors

[2] The changing risk and burden of wildfire in the united states 2021

[3] Spectraldiff: A generative framework for hyperspectral image classification with diffusion models 2023

[4] Ambient diffusion: Learning clean distributions from corrupted data 2023

[5] de Ara \'u jo, B. M. P. B., von Bloh, M., Rupprecht, V., Schaefer, H., and Asseng, S. Bird’s-eye view: Remote sensing insights into the impact of mowing events on eurasian curlew habitat selection. Ag 2025

[6] L., Xu, F., Hu, Y., B \"o sch, H., Landgraf, J., and Li, Z 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:08.187046Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e8de5b0d3b036688a24c9e646d6a14cf9b9e4d18cb501e409a46df2fb63c1d24

Aliases

arxiv: 2605.14341 · arxiv_version: 2605.14341v1 · doi: 10.48550/arxiv.2605.14341 · pith_short_12: 5DPFWDJ3ANTI · pith_short_16: 5DPFWDJ3ANTIRISM · pith_short_8: 5DPFWDJ3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/5DPFWDJ3ANTIRISMTZSG22QUZ6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e8de5b0d3b036688a24c9e646d6a14cf9b9e4d18cb501e409a46df2fb63c1d24

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "93dc5fbc405703f11c8204d7a25a96ad796a2f41b324f49fd48c42ec274692d7",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T04:04:34Z",
    "title_canon_sha256": "d11786dd9722120aff9b87d21c4f9fc34cd6a77a6bed26445989897bb778acd3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14341",
    "kind": "arxiv",
    "version": 1
  }
}