Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition Article Swipe
K. Henneberger
,
Jing Qin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.00951
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.00951
Hyperspectral Imaging (HSI) serves as an important technique in remote sensing. However, high dimensionality and data volume typically pose significant computational challenges. Band selection is essential for reducing spectral redundancy in hyperspectral imagery while retaining intrinsic critical information. In this work, we propose a novel hyperspectral band selection model by decomposing the data into a low-rank and smooth component and a sparse one. In particular, we develop a generalized 3D total variation (G3DTV) by applying the $\ell_1^p$-norm to derivatives to preserve spatial-spectral smoothness. By employing the alternating direction method of multipliers (ADMM), we derive an efficient algorithm, where the tensor low-rankness is implied by the tensor CUR decomposition. We demonstrate the effectiveness of the proposed approach through comparisons with various other state-of-the-art band selection techniques using two benchmark real-world datasets. In addition, we provide practical guidelines for parameter selection in both noise-free and noisy scenarios.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.00951
- https://arxiv.org/pdf/2405.00951
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396650767
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396650767Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2405.00951Digital Object Identifier
- Title
-
Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR DecompositionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-02Full publication date if available
- Authors
-
K. Henneberger, Jing QinList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.00951Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2405.00951Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2405.00951Direct OA link when available
- Concepts
-
Hyperspectral imaging, Selection (genetic algorithm), Decomposition, Tensor (intrinsic definition), Tensor decomposition, Mathematics, Computer science, Artificial intelligence, Pattern recognition (psychology), Remote sensing, Geology, Chemistry, Pure mathematics, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4396650767 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2405.00951 |
| ids.doi | https://doi.org/10.48550/arxiv.2405.00951 |
| ids.openalex | https://openalex.org/W4396650767 |
| fwci | |
| type | preprint |
| title | Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10688 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9940999746322632 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Image and Signal Denoising Methods |
| topics[1].id | https://openalex.org/T10500 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.989799976348877 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2206 |
| topics[1].subfield.display_name | Computational Mechanics |
| topics[1].display_name | Sparse and Compressive Sensing Techniques |
| topics[2].id | https://openalex.org/T13717 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9728000164031982 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2207 |
| topics[2].subfield.display_name | Control and Systems Engineering |
| topics[2].display_name | Advanced Algorithms and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C159078339 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8409838676452637 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q959005 |
| concepts[0].display_name | Hyperspectral imaging |
| concepts[1].id | https://openalex.org/C81917197 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6528074741363525 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q628760 |
| concepts[1].display_name | Selection (genetic algorithm) |
| concepts[2].id | https://openalex.org/C124681953 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6473119258880615 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q339062 |
| concepts[2].display_name | Decomposition |
| concepts[3].id | https://openalex.org/C155281189 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5468184947967529 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3518150 |
| concepts[3].display_name | Tensor (intrinsic definition) |
| concepts[4].id | https://openalex.org/C2986737658 |
| concepts[4].level | 3 |
| concepts[4].score | 0.4476461112499237 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q30103009 |
| concepts[4].display_name | Tensor decomposition |
| concepts[5].id | https://openalex.org/C33923547 |
| concepts[5].level | 0 |
| concepts[5].score | 0.4313525855541229 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[5].display_name | Mathematics |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.410249263048172 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.40591320395469666 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C153180895 |
| concepts[8].level | 2 |
| concepts[8].score | 0.35938042402267456 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[8].display_name | Pattern recognition (psychology) |
| concepts[9].id | https://openalex.org/C62649853 |
| concepts[9].level | 1 |
| concepts[9].score | 0.34179937839508057 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[9].display_name | Remote sensing |
| concepts[10].id | https://openalex.org/C127313418 |
| concepts[10].level | 0 |
| concepts[10].score | 0.3001638948917389 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[10].display_name | Geology |
| concepts[11].id | https://openalex.org/C185592680 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2719448506832123 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[11].display_name | Chemistry |
| concepts[12].id | https://openalex.org/C202444582 |
| concepts[12].level | 1 |
| concepts[12].score | 0.2278449535369873 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[12].display_name | Pure mathematics |
| concepts[13].id | https://openalex.org/C178790620 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11351 |
| concepts[13].display_name | Organic chemistry |
| keywords[0].id | https://openalex.org/keywords/hyperspectral-imaging |
| keywords[0].score | 0.8409838676452637 |
| keywords[0].display_name | Hyperspectral imaging |
| keywords[1].id | https://openalex.org/keywords/selection |
| keywords[1].score | 0.6528074741363525 |
| keywords[1].display_name | Selection (genetic algorithm) |
| keywords[2].id | https://openalex.org/keywords/decomposition |
| keywords[2].score | 0.6473119258880615 |
| keywords[2].display_name | Decomposition |
| keywords[3].id | https://openalex.org/keywords/tensor |
| keywords[3].score | 0.5468184947967529 |
| keywords[3].display_name | Tensor (intrinsic definition) |
| keywords[4].id | https://openalex.org/keywords/tensor-decomposition |
| keywords[4].score | 0.4476461112499237 |
| keywords[4].display_name | Tensor decomposition |
| keywords[5].id | https://openalex.org/keywords/mathematics |
| keywords[5].score | 0.4313525855541229 |
| keywords[5].display_name | Mathematics |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.410249263048172 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.40591320395469666 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/pattern-recognition |
| keywords[8].score | 0.35938042402267456 |
| keywords[8].display_name | Pattern recognition (psychology) |
| keywords[9].id | https://openalex.org/keywords/remote-sensing |
| keywords[9].score | 0.34179937839508057 |
| keywords[9].display_name | Remote sensing |
| keywords[10].id | https://openalex.org/keywords/geology |
| keywords[10].score | 0.3001638948917389 |
| keywords[10].display_name | Geology |
| keywords[11].id | https://openalex.org/keywords/chemistry |
| keywords[11].score | 0.2719448506832123 |
| keywords[11].display_name | Chemistry |
| keywords[12].id | https://openalex.org/keywords/pure-mathematics |
| keywords[12].score | 0.2278449535369873 |
| keywords[12].display_name | Pure mathematics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2405.00951 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2405.00951 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2405.00951 |
| locations[1].id | doi:10.48550/arxiv.2405.00951 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2405.00951 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5113198214 |
| authorships[0].author.orcid | https://orcid.org/0009-0008-2360-7995 |
| authorships[0].author.display_name | K. Henneberger |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Henneberger, Katherine |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5066509235 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8630-2904 |
| authorships[1].author.display_name | Jing Qin |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Qin, Jing |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2405.00951 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-05-05T00:00:00 |
| display_name | Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10688 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9940999746322632 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Image and Signal Denoising Methods |
| related_works | https://openalex.org/W4379256054, https://openalex.org/W2093953080, https://openalex.org/W2911706637, https://openalex.org/W47805180, https://openalex.org/W3216281372, https://openalex.org/W2963838862, https://openalex.org/W2608089480, https://openalex.org/W3015641590, https://openalex.org/W2987657992, https://openalex.org/W4297666106 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2405.00951 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2405.00951 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2405.00951 |
| primary_location.id | pmh:oai:arXiv.org:2405.00951 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2405.00951 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2405.00951 |
| publication_date | 2024-05-02 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 43, 54, 60, 67 |
| abstract_inverted_index.3D | 69 |
| abstract_inverted_index.By | 83 |
| abstract_inverted_index.In | 38, 63, 130 |
| abstract_inverted_index.We | 108 |
| abstract_inverted_index.an | 5, 94 |
| abstract_inverted_index.as | 4 |
| abstract_inverted_index.by | 49, 73, 103 |
| abstract_inverted_index.in | 8, 30, 139 |
| abstract_inverted_index.is | 24, 101 |
| abstract_inverted_index.of | 89, 112 |
| abstract_inverted_index.to | 77, 79 |
| abstract_inverted_index.we | 41, 65, 92, 132 |
| abstract_inverted_index.CUR | 106 |
| abstract_inverted_index.and | 14, 56, 59, 142 |
| abstract_inverted_index.for | 26, 136 |
| abstract_inverted_index.the | 51, 75, 85, 98, 104, 110, 113 |
| abstract_inverted_index.two | 126 |
| abstract_inverted_index.Band | 22 |
| abstract_inverted_index.band | 46, 122 |
| abstract_inverted_index.both | 140 |
| abstract_inverted_index.data | 15, 52 |
| abstract_inverted_index.high | 12 |
| abstract_inverted_index.into | 53 |
| abstract_inverted_index.one. | 62 |
| abstract_inverted_index.pose | 18 |
| abstract_inverted_index.this | 39 |
| abstract_inverted_index.with | 118 |
| abstract_inverted_index.(HSI) | 2 |
| abstract_inverted_index.model | 48 |
| abstract_inverted_index.noisy | 143 |
| abstract_inverted_index.novel | 44 |
| abstract_inverted_index.other | 120 |
| abstract_inverted_index.total | 70 |
| abstract_inverted_index.using | 125 |
| abstract_inverted_index.where | 97 |
| abstract_inverted_index.while | 33 |
| abstract_inverted_index.work, | 40 |
| abstract_inverted_index.derive | 93 |
| abstract_inverted_index.method | 88 |
| abstract_inverted_index.remote | 9 |
| abstract_inverted_index.serves | 3 |
| abstract_inverted_index.smooth | 57 |
| abstract_inverted_index.sparse | 61 |
| abstract_inverted_index.tensor | 99, 105 |
| abstract_inverted_index.volume | 16 |
| abstract_inverted_index.(ADMM), | 91 |
| abstract_inverted_index.(G3DTV) | 72 |
| abstract_inverted_index.Imaging | 1 |
| abstract_inverted_index.develop | 66 |
| abstract_inverted_index.imagery | 32 |
| abstract_inverted_index.implied | 102 |
| abstract_inverted_index.propose | 42 |
| abstract_inverted_index.provide | 133 |
| abstract_inverted_index.through | 116 |
| abstract_inverted_index.various | 119 |
| abstract_inverted_index.However, | 11 |
| abstract_inverted_index.applying | 74 |
| abstract_inverted_index.approach | 115 |
| abstract_inverted_index.critical | 36 |
| abstract_inverted_index.low-rank | 55 |
| abstract_inverted_index.preserve | 80 |
| abstract_inverted_index.proposed | 114 |
| abstract_inverted_index.reducing | 27 |
| abstract_inverted_index.sensing. | 10 |
| abstract_inverted_index.spectral | 28 |
| abstract_inverted_index.addition, | 131 |
| abstract_inverted_index.benchmark | 127 |
| abstract_inverted_index.component | 58 |
| abstract_inverted_index.datasets. | 129 |
| abstract_inverted_index.direction | 87 |
| abstract_inverted_index.efficient | 95 |
| abstract_inverted_index.employing | 84 |
| abstract_inverted_index.essential | 25 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.intrinsic | 35 |
| abstract_inverted_index.parameter | 137 |
| abstract_inverted_index.practical | 134 |
| abstract_inverted_index.retaining | 34 |
| abstract_inverted_index.selection | 23, 47, 123, 138 |
| abstract_inverted_index.technique | 7 |
| abstract_inverted_index.typically | 17 |
| abstract_inverted_index.variation | 71 |
| abstract_inverted_index.algorithm, | 96 |
| abstract_inverted_index.guidelines | 135 |
| abstract_inverted_index.noise-free | 141 |
| abstract_inverted_index.real-world | 128 |
| abstract_inverted_index.redundancy | 29 |
| abstract_inverted_index.scenarios. | 144 |
| abstract_inverted_index.techniques | 124 |
| abstract_inverted_index.alternating | 86 |
| abstract_inverted_index.challenges. | 21 |
| abstract_inverted_index.comparisons | 117 |
| abstract_inverted_index.decomposing | 50 |
| abstract_inverted_index.demonstrate | 109 |
| abstract_inverted_index.derivatives | 78 |
| abstract_inverted_index.generalized | 68 |
| abstract_inverted_index.multipliers | 90 |
| abstract_inverted_index.particular, | 64 |
| abstract_inverted_index.significant | 19 |
| abstract_inverted_index.smoothness. | 82 |
| abstract_inverted_index.information. | 37 |
| abstract_inverted_index.low-rankness | 100 |
| abstract_inverted_index.Hyperspectral | 0 |
| abstract_inverted_index.computational | 20 |
| abstract_inverted_index.effectiveness | 111 |
| abstract_inverted_index.hyperspectral | 31, 45 |
| abstract_inverted_index.decomposition. | 107 |
| abstract_inverted_index.dimensionality | 13 |
| abstract_inverted_index.$\ell_1^p$-norm | 76 |
| abstract_inverted_index.spatial-spectral | 81 |
| abstract_inverted_index.state-of-the-art | 121 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |