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Paper Citation Record · LEDGER

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification

As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2504.15155.

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

pith.paper-citation-record.v1
2504.15155 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:35:37.532173Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:53:11.229842Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:53:14.107590Z

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

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Outbound references

Observation 7b016586-1165-4d93-b091-6c19d26f1fbd · outbound

This paper cites Intelligent remote sensing satellite system.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Intelligent remote sensing satellite system

Reference 1

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Observation ca84407b-6f34-46e1-b433-f1c430fea3a7 · outbound

This paper cites Hyperspectral imaging: techniques for spectral detection and classification, volume 1.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Hyperspectral imaging: techniques for spectral detection and classification, volume 1

Reference 2

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Observation 72b59098-800b-4a86-a7a5-ef7038e69c47 · outbound

This paper cites Deep fea- ture extraction and classification of hyperspectral images based on convolutional neural networks.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Deep fea- ture extraction and classification of hyperspectral images based on convolutional neural networks

Reference 3

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Observation e88552cb-9d22-4039-8c26-634b69f3c8b7 · outbound

This paper cites Spectralformer: Rethinking hyperspectral image classification with transform- ers.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Spectralformer: Rethinking hyperspectral image classification with transform- ers

Reference 4

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Observation 57b60268-5066-4910-9b27-dabc6f5a725c · outbound

This paper cites Densely connected convolutional networks.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Densely connected convolutional networks

Reference 5

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Observation 866404b7-de16-4a42-b623-9dfcf3c2a67f · outbound

This paper cites Going deeper with contextual cnn for hyperspectral image classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Going deeper with contextual cnn for hyperspectral image classification

Reference 6

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Observation ce322e4e-650f-4b82-ac02-f475e79d121c · outbound

This paper cites DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification

Reference 7

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Observation 783c2f32-99d3-4365-9cb7-6082407f1f08 · outbound

This paper cites 3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification 3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification

Reference 8

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Observation fa7699fc-b61e-4136-86c3-139114ee3d02 · outbound

This paper cites Efficient Dynamic Attention 3D Convolution for Hyperspectral Image Classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Efficient Dynamic Attention 3D Convolution for Hyperspectral Image Classification

Reference 9

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Observation f02028c2-97d9-442e-af0e-1cd0ea9df51e · outbound

This paper cites Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image Classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image Classification

Reference 10

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Observation 7fd5b3f8-e43a-4601-b224-58d0663afb0a · outbound

This paper cites Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image Classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image Classification

Reference 11

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Observation 3bee4c47-5ef3-41db-98d8-ea043602e662 · outbound

This paper cites Spatial-spectral hyperspectral classification based on learnable 3d group convolution.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Spatial-spectral hyperspectral classification based on learnable 3d group convolution

Reference 12

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Observation 53f73e5d-c37f-43fa-9310-71bed61372f5 · outbound

This paper cites Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks

Reference 13

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Observation d1da3e87-3c2a-4500-a165-a536abf108be · outbound

This paper cites Doubleconvpool-structured 3d-cnn for hyper- spectral remote sensing image classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Doubleconvpool-structured 3d-cnn for hyper- spectral remote sensing image classification

Reference 14

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Observation 523a06c7-f0ef-40d7-8223-264e995f6b47 · outbound

This paper cites Hyperspectral remote sensing image classification using three-dimensional-squeeze-and- excitation-densenet (3d-se-densenet).

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Hyperspectral remote sensing image classification using three-dimensional-squeeze-and- excitation-densenet (3d-se-densenet)

Reference 15

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Observation 23bacc53-e8e6-4c9f-ab7a-c73f4647b1c5 · outbound

This paper cites Scene classi- fication of high-resolution remote sensing image using transfer learning with multi-model feature extraction framework.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Scene classi- fication of high-resolution remote sensing image using transfer learning with multi-model feature extraction framework

Reference 16

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Observation 1f7d6f1b-f581-4a9d-83fb-9cbf537caded · outbound

This paper cites Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification

Reference 17

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Observation 794768cd-aa5d-4986-812d-77672a9c40a8 · outbound

This paper cites Learning efficient convolutional networks through network slimming.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Learning efficient convolutional networks through network slimming

Reference 18

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Observation 6a7134a0-918d-46cc-8717-0e82e468c073 · outbound

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

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Swin transformer: Hierarchical vision transformer using shifted windows

Reference 19

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Observation ca3ff46a-29ea-44f6-980a-e816ebbd2ebc · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification KAN: Kolmogorov-Arnold Networks

Reference 20

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Observation a7e80c07-629c-45d4-9994-3ae5f8a7f4c6 · outbound

This paper cites Deep supervised learning for hyperspectral data classification through con- volutional neural networks.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Deep supervised learning for hyperspectral data classification through con- volutional neural networks

Reference 21

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Observation f5119571-a37c-4cfc-9518-9b578f9af6c3 · outbound

This paper cites Spectral–spatial feature tokeniza- tion transformer for hyperspectral image classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Spectral–spatial feature tokeniza- tion transformer for hyperspectral image classification

Reference 22

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Observation 075dbe5f-b1b7-4530-b3be-2b309fa0c5b9 · outbound

This paper cites Kolmogorov- arnold networks (kans) for time series analysis.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Kolmogorov- arnold networks (kans) for time series analysis

Reference 23

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Observation 15c8f469-0c84-4ea2-86b2-99f05e76b98b · outbound

This paper cites A fast dense spectral– spatial convolution network framework for hyperspectral images classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification A fast dense spectral– spatial convolution network framework for hyperspectral images classification

Reference 24

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Observation d788c64d-2398-4c5c-8648-ace272b88df7 · outbound

This paper cites Multi-scale dense networks for hyperspec- tral remote sensing image classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Multi-scale dense networks for hyperspec- tral remote sensing image classification

Reference 25

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Observation 806a4bad-a172-40d0-a2a0-fb923e5bc193 · outbound

This paper cites Three-dimensional densely connected convolutional network for hyperspectral remote sensing image classifi- cation.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Three-dimensional densely connected convolutional network for hyperspectral remote sensing image classifi- cation

Reference 26

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This paper cites Deep feature aggregation network for hyperspectral remote sensing image classification.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Deep feature aggregation network for hyperspectral remote sensing image classification

Reference 27

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Observation 712fb81a-29ad-44a3-a3e0-48700c5e455d · outbound

This paper cites Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach.

Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach

Reference 28

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Pith citing papers

Observation 51bfb8f6-c1e0-47c8-8d83-38046fe6128b · inbound

MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture cites this paper.

MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification

Reference 14

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