SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation | Paper Citation Record

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This paper cites Ranking via Sinkhorn Propagation.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Ranking via Sinkhorn Propagation

Reference 1

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This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Self-supervised learning from images with a joint-embedding predictive architecture

Reference 2

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This paper cites Hyperkon: A self-supervised contrastive network for hyperspectral image analysis.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Hyperkon: A self-supervised contrastive network for hyperspectral image analysis

Reference 3

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This paper cites Curriculum learning.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Curriculum learning

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This paper cites Using enmap satellite and machine learning for soil organic carbon assessment: A case study in semi-arid region.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Using enmap satellite and machine learning for soil organic carbon assessment: A case study in semi-arid region

Reference 5

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This paper cites Transformer- based masked autoencoder with contrastive loss for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Transformer- based masked autoencoder with contrastive loss for hyperspectral image classification

Reference 6

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This paper cites Mask-enhanced contrastive learning for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Mask-enhanced contrastive learning for hyperspectral image classification

Reference 7

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This paper cites The enmap spaceborne imaging spectroscopy mission: Initial scientific results two years after launch.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation The enmap spaceborne imaging spectroscopy mission: Initial scientific results two years after launch

Reference 8

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This paper cites Laird, Maurice J.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Laird, Maurice J

Reference 9

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This paper cites A simple framework for contrastive learning of visual representations.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation A simple framework for contrastive learning of visual representations

Reference 10

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This paper cites Exploring simple siamese representation learning.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Exploring simple siamese representation learning

Reference 11

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This paper cites Xception: Deep learning with depthwise separable convolutions.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Xception: Deep learning with depthwise separable convolutions

Reference 12

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This paper cites Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models

Reference 13

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This paper cites Bayesian optimization over permutation spaces.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Bayesian optimization over permutation spaces

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 15

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SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Unresolved cited work

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This paper cites Self-supervised video representation learning with odd-one-out networks.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Self-supervised video representation learning with odd-one-out networks

Reference 17

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This paper cites Dual attention network for scene segmentation.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Dual attention network for scene segmentation

Reference 18

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This paper cites Gholizadeh, M.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Gholizadeh, M

Reference 19

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SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Unresolved cited work

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This paper cites Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko

Reference 21

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This paper cites Cross-domain contrastive learning for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Cross-domain contrastive learning for hyperspectral image classification

Reference 22

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This paper cites On the power of curriculum learning in training deep networks.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation On the power of curriculum learning in training deep networks

Reference 23

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This paper cites Momentum contrast for unsupervised visual representation learning.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Momentum contrast for unsupervised visual representation learning

Reference 24

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This paper cites Masked autoencoders are scalable vision learners.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Masked autoencoders are scalable vision learners

Reference 25

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This paper cites Spectralformer: Rethinking hyperspectral image classification with transformers.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Spectralformer: Rethinking hyperspectral image classification with transformers

Reference 26

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This paper cites Hyper- spectral imagery classification based on contrastive learning.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Hyper- spectral imagery classification based on contrastive learning

Reference 27

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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This paper cites Contrastive learning based on transformer for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Contrastive learning based on transformer for hyperspectral image classification

Reference 29

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This paper cites Spectral–spatial masked transformer with supervised and contrastive learning for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Spectral–spatial masked transformer with supervised and contrastive learning for hyperspectral image classification

Reference 30

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This paper cites Instructional mask autoencoder: A scalable learner for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Instructional mask autoencoder: A scalable learner for hyperspectral image classification

Reference 31

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This paper cites Soil carbon sequestration impacts on global climate change and food security.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Soil carbon sequestration impacts on global climate change and food security

Reference 32

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This paper cites Demae: Diffusion enhanced masked autoencoder for hyperspectral image classification with few labeled samples.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Demae: Diffusion enhanced masked autoencoder for hyperspectral image classification with few labeled samples

Reference 33

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This paper cites Self-supervised feature learning based on spectral masking for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Self-supervised feature learning based on spectral masking for hyperspectral image classification

Reference 34

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This paper cites Lawrence Zitnick, and Martial Hebert.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Lawrence Zitnick, and Martial Hebert

Reference 35

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This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 36

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This paper cites Paoletti, Juan M.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Paoletti, Juan M

Reference 37

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This paper cites Integrating soil spectral library and prisma data to estimate soil organic carbon in crop lands.IEEE Geoscience and Remote Sensing Letters, 2024.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Integrating soil spectral library and prisma data to estimate soil organic carbon in crop lands.IEEE Geoscience and Remote Sensing Letters, 2024

Reference 38

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This paper cites Guided curriculum model adap- tation and uncertainty-aware evaluation for semantic nighttime image segmentation.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Guided curriculum model adap- tation and uncertainty-aware evaluation for semantic nighttime image segmentation

Reference 39

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This paper cites Shaw and Heidi-Marie K.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Shaw and Heidi-Marie K

Reference 40

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This paper cites Cur- riculum learning: A survey.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Cur- riculum learning: A survey

Reference 41

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This paper cites Viscarra Rossel, Abdul M.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Viscarra Rossel, Abdul M

Reference 42

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This paper cites Prediction of soil organic carbon at the european scale by visible and near infrared reflectance spectroscopy.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Prediction of soil organic carbon at the european scale by visible and near infrared reflectance spectroscopy

Reference 43

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SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Unresolved cited work

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This paper cites Spectral–spatial feature to- kenization transformer for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Spectral–spatial feature to- kenization transformer for hyperspectral image classification

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:06:07.016002Z

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Observation 844069c5-f8a3-4dd3-a42b-dc1e340a1f47 · outbound

This paper cites Hsimae: A unified masked autoencoder with large-scale pre-training for hyperspectral image classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Hsimae: A unified masked autoencoder with large-scale pre-training for hyperspectral image classification

Reference 46

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This paper cites The enmap imaging spectroscopy mission towards operations.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation The enmap imaging spectroscopy mission towards operations

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:06:07.155134Z

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Observation b5af0ff5-9fe4-49c4-9ecd-ab41edf83ffa · outbound

This paper cites Xlnet: Generalized autoregressive pretraining for language understanding.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Xlnet: Generalized autoregressive pretraining for language understanding

Reference 48

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This paper cites Cross-domain few-shot contrastive learning for hyperspectral images classification.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Cross-domain few-shot contrastive learning for hyperspectral images classification

Reference 49

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This paper cites Cbam: Convolu- tional block attention module.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Cbam: Convolu- tional block attention module

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:06:06.809299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 371abe5b-c7c5-4e55-aec7-7a81b73e9465 · outbound

This paper cites an unresolved cited work.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Unresolved cited work

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T14:06:03.314609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 281e653d-210f-49ad-abcb-3a4089cc0411 · outbound

This paper cites an unresolved cited work.

SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation Unresolved cited work

Reference 2022

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T14:06:06.164070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers