Paper Citation Record · LEDGER

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation

As of 7 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:1907.11935.

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

pith.paper-citation-record.v1
1907.11935 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T14:42:43.952026Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

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verified fuzzy20
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9725b6d5-4cc5-4b4d-9d95-09087b6a7bf5 · outbound

This paper cites Modern trends in hyperspectral image analysis: A review.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Modern trends in hyperspectral image analysis: A review

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.581677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b883147-776a-4f55-ad24-bfd652579774 · outbound

This paper cites Segmentation of hyperspectral images via subtractive clustering and cluster validation using one-class SVMs.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Segmentation of hyperspectral images via subtractive clustering and cluster validation using one-class SVMs

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.577978Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13d1932f-dfef-4ace-9699-ec3b81872070 · outbound

This paper cites Sparse representation-based hyperspectral image classiﬁcation using multiscale superpixels and guided ﬁlter.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Sparse representation-based hyperspectral image classiﬁcation using multiscale superpixels and guided ﬁlter

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.573622Z

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Observation 8134de76-958a-4606-b545-89b58806b94d · outbound

This paper cites Spectralspatial classiﬁcation of hyper- spectral data based on deep belief network.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Spectralspatial classiﬁcation of hyper- spectral data based on deep belief network

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.569850Z

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Observation 18ca9534-2320-44d9-9d11-0dc994e9a182 · outbound

This paper cites Spectral-spatial feature extraction for hyperspectral image classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Spectral-spatial feature extraction for hyperspectral image classiﬁcation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.639121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 086fc29b-0abe-4f7a-ae7b-7894e62725e9 · outbound

This paper cites Learning to diversify deep belief networks for hyperspectral image classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Learning to diversify deep belief networks for hyperspectral image classiﬁcation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.643459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 39064472-f178-451d-8cd7-74deb0573159 · outbound

This paper cites Deep recurrent nets for hyperspec- tral classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Deep recurrent nets for hyperspec- tral classiﬁcation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.626344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67c36cac-feac-44bb-b389-3a4841c8515c · outbound

This paper cites BASS Net: Band-adaptive spectral-spatial feature learning neural network for hyperspectral image classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation BASS Net: Band-adaptive spectral-spatial feature learning neural network for hyperspectral image classiﬁcation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.630638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9048af14-fda6-4cb5-b9be-ce62259dddc7 · outbound

This paper cites Going deeper with contextual CNN for hyper- spectral classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Going deeper with contextual CNN for hyper- spectral classiﬁcation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.618450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f893876d-d105-460b-95c3-480da5bdce49 · outbound

This paper cites Hyperspectral image classiﬁcation using convolutional neural networks and multiple feature learning.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Hyperspectral image classiﬁcation using convolutional neural networks and multiple feature learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.608406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 47400d36-23de-4f94-b9d8-00c3823a2055 · outbound

This paper cites Deep learning for hyperspectral image classiﬁcation: An overview.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Deep learning for hyperspectral image classiﬁcation: An overview

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.611997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2fcded3-a4fe-4f00-97e2-ed93f7b72b85 · outbound

This paper cites Hyperspectral image classiﬁcation with deep learning models.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Hyperspectral image classiﬁcation with deep learning models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.622677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ace07243-6536-485d-bff3-83c676f90f64 · outbound

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

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Deep feature extraction and classiﬁcation of hyperspectral images based on convolutional neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.634681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de3b76dd-c7e6-41af-8ac9-5011c5da5c20 · outbound

This paper cites Spectralspatial residual network for hyperspectral image classiﬁcation: A 3-d deep learning framework.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Spectralspatial residual network for hyperspectral image classiﬁcation: A 3-d deep learning framework

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.593054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 86c48d75-a689-4374-8c81-dc59050328c6 · outbound

This paper cites 3-d deep learning approach for remote sensing image classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation 3-d deep learning approach for remote sensing image classiﬁcation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.601226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0d17bf2-4977-455a-a830-4c1ee17fc202 · outbound

This paper cites A new deep convolutional neural network for fast hyperspectral image classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation A new deep convolutional neural network for fast hyperspectral image classiﬁcation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.597115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4b899278-a902-4a52-9b92-f05dce313aa5 · outbound

This paper cites Validating hyperspectral image segmentation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Validating hyperspectral image segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.604963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80fd2cc1-a2be-4c36-93e2-4a6f700808b2 · outbound

This paper cites Unsupervised spectralspatial feature learning via deep residual convdeconv network for hyperspectral image classiﬁcation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Unsupervised spectralspatial feature learning via deep residual convdeconv network for hyperspectral image classiﬁcation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.585559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 19c7c586-5a5d-4646-a607-cfca25a8e485 · outbound

This paper cites Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-24T14:44:36.372409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c9f1262-2963-44d9-9dbb-c7d37e83319a · outbound

This paper cites Training- and test-time data augmentation for hyperspectral image segmentation.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Training- and test-time data augmentation for hyperspectral image segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.588829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0bf3174e-756e-415d-bcca-f716dc8ba36d · outbound

This paper cites Active learning with convolutional neural networks for hyperspectral image classiﬁcation using a new bayesian approach.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation Active learning with convolutional neural networks for hyperspectral image classiﬁcation using a new bayesian approach

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T14:44:37.647518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.