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pith:ZTY6PAS3

pith:2026:ZTY6PAS3Q3YG3T24OXJC6YN6HT

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Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks

Ali Zia, Guanyiman Fu, Jing Wang, Judy X. Yang, Jun Zhou, Zekun Long

Kolmogorov-Arnold Networks with learnable splines and LiDAR-guided modules improve hyperspectral-LiDAR fusion accuracy.

arxiv:2605.14239 v1 · 2026-05-14 · cs.CV

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Claims

C1strongest claim

Experiments on the Houston 2013 and MUUFL benchmarks demonstrate that IFGNet consistently outperforms existing fusion methods in overall accuracy, average accuracy, and Cohen's Kappa, while maintaining an efficient architecture.

C2weakest assumption

That the learnable spline functions inside the KAN layers, together with the LiDAR-guided implicit aggregation modules, are the primary drivers of the observed accuracy gains rather than differences in training protocol, data augmentation, or hyper-parameter tuning.

C3one line summary

IFGNet replaces fixed activations with KAN splines and adds LiDAR-guided implicit aggregation in spatial and frequency domains, reporting higher overall accuracy, average accuracy, and Kappa than prior fusion methods on the Houston 2013 and MUUFL benchmarks.

References

23 extracted · 23 resolved · 0 Pith anchors

[1] Clas- sification of hyperspectral data from urban areas based on extended morphological profiles, 2005

[2] A novel deep learning framework by combination of subspace- based feature extraction and convolutional neural net- works for hyperspectral images classification, 2018

[3] Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review, 2020

[4] Hyperspectral image analysis. a tutorial, 2015

[5] Deep learning for hyperspectral image classification: An overview, 2019

Receipt and verification

First computed	2026-05-17T23:39:10.667876Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ccf1e7825b86f06dcf5c75d22f61be3cd071df0a530b8b11568017766e7d3e82

Aliases

arxiv: 2605.14239 · arxiv_version: 2605.14239v1 · doi: 10.48550/arxiv.2605.14239 · pith_short_12: ZTY6PAS3Q3YG · pith_short_16: ZTY6PAS3Q3YG3T24 · pith_short_8: ZTY6PAS3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZTY6PAS3Q3YG3T24OXJC6YN6HT \
  | 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: ccf1e7825b86f06dcf5c75d22f61be3cd071df0a530b8b11568017766e7d3e82

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T01:12:12Z",
    "title_canon_sha256": "00221b883e97e99c3105b83daf0a3ed54afb107765ba771a55b5d83e0ee46666"
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    "kind": "arxiv",
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