A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification Article Swipe
Dongxu Liu
,
Guangliang Han
,
Peixun Liu
,
Hang Yang
,
Dianbing Chen
,
Qingqing Li
,
Jiajia Wu
,
Yirui Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14112678
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14112678
Due to end-to-end optimization characteristics and fine generalization ability, convolutional neural networks have been widely applied to hyperspectral image (HSI) classification, playing an irreplaceable role. However, previous studies struggle with two major challenges: (1) HSI contains complex topographic features, the number of labeled samples in different categories is unbalanced, resulting in poor classification for categories with few labeled samples; (2) With the deepening of neural network models, it is difficult to extract more discriminative spectral-spatial features. To address the issues mentioned above, we propose a discriminative spectral-spatial-semantic feature network based on shuffle and frequency attention mechanisms for HSI classification. There are four main parts of our approach: spectral-spatial shuffle attention module (SSAM), context-aware high-level spectral-spatial feature extraction module (CHSFEM), spectral-spatial frequency attention module (SFAM), and cross-connected semantic feature extraction module (CSFEM). First, to fully excavate the category attribute information, SSAM based on a “Deconstruction-Reconstruction” structure is designed, solving the problem of poor classification performance caused by an unbalanced number of label samples. Considering that deep spectral-spatial features are difficult to extract, CHSFEM and SFAM are constructed. The former is based on the “Horizontal-Vertical” structure to capture context-aware high-level multiscale features. The latter introduces multiple frequency components to compress channels to obtain more multifarious features. Finally, towards suppressing noisy boundaries efficiently and capturing abundant semantic information, CSFEM is devised. Numerous experiments are implemented on four public datasets: the evaluation indexes of OA, AA and Kappa on four datasets all exceed 99%, demonstrating that our method can achieve satisfactory performance and is superior to other contrasting methods.
Related Topics
Concepts
Discriminative model
Computer science
Pattern recognition (psychology)
Artificial intelligence
Hyperspectral imaging
Convolutional neural network
Spatial contextual awareness
Feature (linguistics)
Context (archaeology)
Feature extraction
Image (mathematics)
Generalization
Mathematics
Geography
Archaeology
Mathematical analysis
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14112678
- https://www.mdpi.com/2072-4292/14/11/2678/pdf?version=1654230889
- OA Status
- gold
- Cited By
- 4
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281988103
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4281988103Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14112678Digital Object Identifier
- Title
-
A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-03Full publication date if available
- Authors
-
Dongxu Liu, Guangliang Han, Peixun Liu, Hang Yang, Dianbing Chen, Qingqing Li, Jiajia Wu, Yirui WangList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14112678Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/11/2678/pdf?version=1654230889Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/14/11/2678/pdf?version=1654230889Direct OA link when available
- Concepts
-
Discriminative model, Computer science, Pattern recognition (psychology), Artificial intelligence, Hyperspectral imaging, Convolutional neural network, Spatial contextual awareness, Feature (linguistics), Context (archaeology), Feature extraction, Image (mathematics), Generalization, Mathematics, Geography, Archaeology, Mathematical analysis, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4281988103 |
|---|---|
| doi | https://doi.org/10.3390/rs14112678 |
| ids.doi | https://doi.org/10.3390/rs14112678 |
| ids.openalex | https://openalex.org/W4281988103 |
| fwci | 0.5594121 |
| type | article |
| title | A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification |
| awards[0].id | https://openalex.org/G175063211 |
| awards[0].funder_id | https://openalex.org/F4320327282 |
| awards[0].display_name | |
| awards[0].funder_award_id | 20210201132GX |
| awards[0].funder_display_name | Department of Science and Technology of Jilin Province |
| biblio.issue | 11 |
| biblio.volume | 14 |
| biblio.last_page | 2678 |
| biblio.first_page | 2678 |
| topics[0].id | https://openalex.org/T10689 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2214 |
| topics[0].subfield.display_name | Media Technology |
| topics[0].display_name | Remote-Sensing Image Classification |
| topics[1].id | https://openalex.org/T13890 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9884999990463257 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1902 |
| topics[1].subfield.display_name | Atmospheric Science |
| topics[1].display_name | Remote Sensing and Land Use |
| topics[2].id | https://openalex.org/T11659 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9661999940872192 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2214 |
| topics[2].subfield.display_name | Media Technology |
| topics[2].display_name | Advanced Image Fusion Techniques |
| funders[0].id | https://openalex.org/F4320327282 |
| funders[0].ror | |
| funders[0].display_name | Department of Science and Technology of Jilin Province |
| is_xpac | False |
| apc_list.value | 2500 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2707 |
| apc_paid.value | 2500 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2707 |
| concepts[0].id | https://openalex.org/C97931131 |
| concepts[0].level | 2 |
| concepts[0].score | 0.80968177318573 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5282087 |
| concepts[0].display_name | Discriminative model |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7602353096008301 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7129161953926086 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.68288254737854 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C159078339 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5875893235206604 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q959005 |
| concepts[4].display_name | Hyperspectral imaging |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5820670127868652 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C64754055 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5761167407035828 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7574053 |
| concepts[6].display_name | Spatial contextual awareness |
| concepts[7].id | https://openalex.org/C2776401178 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5743147730827332 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[7].display_name | Feature (linguistics) |
| concepts[8].id | https://openalex.org/C2779343474 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5624790191650391 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[8].display_name | Context (archaeology) |
| concepts[9].id | https://openalex.org/C52622490 |
| concepts[9].level | 2 |
| concepts[9].score | 0.5365941524505615 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1026626 |
| concepts[9].display_name | Feature extraction |
| concepts[10].id | https://openalex.org/C115961682 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4497803747653961 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[10].display_name | Image (mathematics) |
| concepts[11].id | https://openalex.org/C177148314 |
| concepts[11].level | 2 |
| concepts[11].score | 0.44179224967956543 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q170084 |
| concepts[11].display_name | Generalization |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.15535297989845276 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C205649164 |
| concepts[13].level | 0 |
| concepts[13].score | 0.09656992554664612 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[13].display_name | Geography |
| concepts[14].id | https://openalex.org/C166957645 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[14].display_name | Archaeology |
| concepts[15].id | https://openalex.org/C134306372 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[15].display_name | Mathematical analysis |
| concepts[16].id | https://openalex.org/C138885662 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[16].display_name | Philosophy |
| concepts[17].id | https://openalex.org/C41895202 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[17].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/discriminative-model |
| keywords[0].score | 0.80968177318573 |
| keywords[0].display_name | Discriminative model |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7602353096008301 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.7129161953926086 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.68288254737854 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/hyperspectral-imaging |
| keywords[4].score | 0.5875893235206604 |
| keywords[4].display_name | Hyperspectral imaging |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.5820670127868652 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/spatial-contextual-awareness |
| keywords[6].score | 0.5761167407035828 |
| keywords[6].display_name | Spatial contextual awareness |
| keywords[7].id | https://openalex.org/keywords/feature |
| keywords[7].score | 0.5743147730827332 |
| keywords[7].display_name | Feature (linguistics) |
| keywords[8].id | https://openalex.org/keywords/context |
| keywords[8].score | 0.5624790191650391 |
| keywords[8].display_name | Context (archaeology) |
| keywords[9].id | https://openalex.org/keywords/feature-extraction |
| keywords[9].score | 0.5365941524505615 |
| keywords[9].display_name | Feature extraction |
| keywords[10].id | https://openalex.org/keywords/image |
| keywords[10].score | 0.4497803747653961 |
| keywords[10].display_name | Image (mathematics) |
| keywords[11].id | https://openalex.org/keywords/generalization |
| keywords[11].score | 0.44179224967956543 |
| keywords[11].display_name | Generalization |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.15535297989845276 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/geography |
| keywords[13].score | 0.09656992554664612 |
| keywords[13].display_name | Geography |
| language | en |
| locations[0].id | doi:10.3390/rs14112678 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S43295729 |
| locations[0].source.issn | 2072-4292 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2072-4292 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Remote Sensing |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2072-4292/14/11/2678/pdf?version=1654230889 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Remote Sensing |
| locations[0].landing_page_url | https://doi.org/10.3390/rs14112678 |
| locations[1].id | pmh:oai:doaj.org/article:cea586eef2224282963c8877d7b46d41 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Remote Sensing, Vol 14, Iss 11, p 2678 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/cea586eef2224282963c8877d7b46d41 |
| locations[2].id | pmh:oai:mdpi.com:/2072-4292/14/11/2678/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Remote Sensing; Volume 14; Issue 11; Pages: 2678 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/rs14112678 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5100386802 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0691-4025 |
| authorships[0].author.display_name | Dongxu Liu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210165038 |
| authorships[0].affiliations[0].raw_affiliation_string | University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[0].affiliations[1].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[0].institutions[0].ror | https://ror.org/012rct222 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[0].institutions[1].id | https://openalex.org/I19820366 |
| authorships[0].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[0].institutions[1].type | government |
| authorships[0].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[0].institutions[2].id | https://openalex.org/I4210165038 |
| authorships[0].institutions[2].ror | https://ror.org/05qbk4x57 |
| authorships[0].institutions[2].type | education |
| authorships[0].institutions[2].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[0].institutions[2].country_code | CN |
| authorships[0].institutions[2].display_name | University of Chinese Academy of Sciences |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Dongxu Liu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China, University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[1].author.id | https://openalex.org/A5088872755 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3757-7447 |
| authorships[1].author.display_name | Guangliang Han |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[1].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[1].institutions[0].ror | https://ror.org/012rct222 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[1].institutions[1].id | https://openalex.org/I19820366 |
| authorships[1].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[1].institutions[1].type | government |
| authorships[1].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Guangliang Han |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[2].author.id | https://openalex.org/A5103123580 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5611-7502 |
| authorships[2].author.display_name | Peixun Liu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[2].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[2].institutions[0].ror | https://ror.org/012rct222 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[2].institutions[1].id | https://openalex.org/I19820366 |
| authorships[2].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[2].institutions[1].type | government |
| authorships[2].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Peixun Liu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[3].author.id | https://openalex.org/A5088740924 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6027-1337 |
| authorships[3].author.display_name | Hang Yang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[3].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[3].institutions[0].ror | https://ror.org/012rct222 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[3].institutions[1].id | https://openalex.org/I19820366 |
| authorships[3].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[3].institutions[1].type | government |
| authorships[3].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hang Yang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[4].author.id | https://openalex.org/A5034050140 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-7986-1596 |
| authorships[4].author.display_name | Dianbing Chen |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[4].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[4].institutions[0].ror | https://ror.org/012rct222 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[4].institutions[1].id | https://openalex.org/I19820366 |
| authorships[4].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[4].institutions[1].type | government |
| authorships[4].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Dianbing Chen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[5].author.id | https://openalex.org/A5100411157 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-5130-9049 |
| authorships[5].author.display_name | Qingqing Li |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[5].affiliations[0].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I4210165038 |
| authorships[5].affiliations[1].raw_affiliation_string | University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[5].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[5].institutions[0].ror | https://ror.org/012rct222 |
| authorships[5].institutions[0].type | facility |
| authorships[5].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[5].institutions[1].id | https://openalex.org/I19820366 |
| authorships[5].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[5].institutions[1].type | government |
| authorships[5].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[5].institutions[1].country_code | CN |
| authorships[5].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[5].institutions[2].id | https://openalex.org/I4210165038 |
| authorships[5].institutions[2].ror | https://ror.org/05qbk4x57 |
| authorships[5].institutions[2].type | education |
| authorships[5].institutions[2].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[5].institutions[2].country_code | CN |
| authorships[5].institutions[2].display_name | University of Chinese Academy of Sciences |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Qingqing Li |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China, University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[6].author.id | https://openalex.org/A5101605865 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-7667-4878 |
| authorships[6].author.display_name | Jiajia Wu |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210165038 |
| authorships[6].affiliations[0].raw_affiliation_string | University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[6].affiliations[1].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[6].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[6].institutions[0].ror | https://ror.org/012rct222 |
| authorships[6].institutions[0].type | facility |
| authorships[6].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[6].institutions[1].id | https://openalex.org/I19820366 |
| authorships[6].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[6].institutions[1].type | government |
| authorships[6].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[6].institutions[1].country_code | CN |
| authorships[6].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[6].institutions[2].id | https://openalex.org/I4210165038 |
| authorships[6].institutions[2].ror | https://ror.org/05qbk4x57 |
| authorships[6].institutions[2].type | education |
| authorships[6].institutions[2].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[6].institutions[2].country_code | CN |
| authorships[6].institutions[2].display_name | University of Chinese Academy of Sciences |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Jiajia Wu |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China, University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[7].author.id | https://openalex.org/A5006282634 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-5767-3343 |
| authorships[7].author.display_name | Yirui Wang |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210165038 |
| authorships[7].affiliations[0].raw_affiliation_string | University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[7].affiliations[1].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[7].affiliations[1].raw_affiliation_string | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| authorships[7].institutions[0].id | https://openalex.org/I4210088164 |
| authorships[7].institutions[0].ror | https://ror.org/012rct222 |
| authorships[7].institutions[0].type | facility |
| authorships[7].institutions[0].lineage | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Changchun Institute of Optics, Fine Mechanics and Physics |
| authorships[7].institutions[1].id | https://openalex.org/I19820366 |
| authorships[7].institutions[1].ror | https://ror.org/034t30j35 |
| authorships[7].institutions[1].type | government |
| authorships[7].institutions[1].lineage | https://openalex.org/I19820366 |
| authorships[7].institutions[1].country_code | CN |
| authorships[7].institutions[1].display_name | Chinese Academy of Sciences |
| authorships[7].institutions[2].id | https://openalex.org/I4210165038 |
| authorships[7].institutions[2].ror | https://ror.org/05qbk4x57 |
| authorships[7].institutions[2].type | education |
| authorships[7].institutions[2].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[7].institutions[2].country_code | CN |
| authorships[7].institutions[2].display_name | University of Chinese Academy of Sciences |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Yirui Wang |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China, University of Chinese Academy of Sciences, Beijing 100049, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2072-4292/14/11/2678/pdf?version=1654230889 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10689 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2214 |
| primary_topic.subfield.display_name | Media Technology |
| primary_topic.display_name | Remote-Sensing Image Classification |
| related_works | https://openalex.org/W2072166414, https://openalex.org/W3209970181, https://openalex.org/W2060875994, https://openalex.org/W3034375524, https://openalex.org/W4230131218, https://openalex.org/W2404757046, https://openalex.org/W2070598848, https://openalex.org/W2019190440, https://openalex.org/W3034864990, https://openalex.org/W2343470940 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/rs14112678 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S43295729 |
| best_oa_location.source.issn | 2072-4292 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2072-4292 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Remote Sensing |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2072-4292/14/11/2678/pdf?version=1654230889 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Remote Sensing |
| best_oa_location.landing_page_url | https://doi.org/10.3390/rs14112678 |
| primary_location.id | doi:10.3390/rs14112678 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S43295729 |
| primary_location.source.issn | 2072-4292 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2072-4292 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Remote Sensing |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2072-4292/14/11/2678/pdf?version=1654230889 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Remote Sensing |
| primary_location.landing_page_url | https://doi.org/10.3390/rs14112678 |
| publication_date | 2022-06-03 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2582369608, https://openalex.org/W2276858186, https://openalex.org/W2402048088, https://openalex.org/W2507855991, https://openalex.org/W2037328426, https://openalex.org/W2078296814, https://openalex.org/W2087263574, https://openalex.org/W2164325628, https://openalex.org/W3040978759, https://openalex.org/W3120389167, https://openalex.org/W2098057602, https://openalex.org/W3131885763, https://openalex.org/W3132173102, https://openalex.org/W3133349463, https://openalex.org/W3017370955, https://openalex.org/W3132502782, https://openalex.org/W3037875773, https://openalex.org/W3165209762, https://openalex.org/W2162698522, https://openalex.org/W2127229869, https://openalex.org/W2143277109, https://openalex.org/W2754507318, https://openalex.org/W2036842392, https://openalex.org/W1988195734, https://openalex.org/W2097092275, https://openalex.org/W2904955533, https://openalex.org/W2158400785, https://openalex.org/W2768420873, https://openalex.org/W4211065340, https://openalex.org/W2572303978, https://openalex.org/W2803948506, https://openalex.org/W2577238056, https://openalex.org/W6788931878, https://openalex.org/W1521436688, https://openalex.org/W2548340849, https://openalex.org/W2004990382, https://openalex.org/W2887785636, https://openalex.org/W2614256707, https://openalex.org/W2500751094, https://openalex.org/W3037613841, https://openalex.org/W4205102943, https://openalex.org/W2804902458, https://openalex.org/W3036990306, https://openalex.org/W3099102165, https://openalex.org/W3157629104, https://openalex.org/W2752782242, https://openalex.org/W3128036642, https://openalex.org/W3031696400, https://openalex.org/W3043181422, https://openalex.org/W4205615939, https://openalex.org/W2883780447, https://openalex.org/W4214666412, https://openalex.org/W2792332881, https://openalex.org/W3008789903, https://openalex.org/W2620547787, https://openalex.org/W2963366243, https://openalex.org/W2989871747, https://openalex.org/W6790146969, https://openalex.org/W6791838446, https://openalex.org/W2914331134, https://openalex.org/W2992919850, https://openalex.org/W3128571866, https://openalex.org/W3103753223, https://openalex.org/W3133682369, https://openalex.org/W3122480337 |
| referenced_works_count | 65 |
| abstract_inverted_index.a | 84, 142 |
| abstract_inverted_index.AA | 231 |
| abstract_inverted_index.To | 76 |
| abstract_inverted_index.an | 22, 156 |
| abstract_inverted_index.by | 155 |
| abstract_inverted_index.in | 44, 50 |
| abstract_inverted_index.is | 47, 68, 145, 178, 216, 249 |
| abstract_inverted_index.it | 67 |
| abstract_inverted_index.of | 41, 63, 104, 150, 159, 229 |
| abstract_inverted_index.on | 90, 141, 180, 222, 234 |
| abstract_inverted_index.to | 1, 16, 70, 132, 169, 184, 196, 199, 251 |
| abstract_inverted_index.we | 82 |
| abstract_inverted_index.(1) | 33 |
| abstract_inverted_index.(2) | 59 |
| abstract_inverted_index.Due | 0 |
| abstract_inverted_index.HSI | 34, 97 |
| abstract_inverted_index.OA, | 230 |
| abstract_inverted_index.The | 176, 190 |
| abstract_inverted_index.all | 237 |
| abstract_inverted_index.and | 5, 92, 124, 172, 210, 232, 248 |
| abstract_inverted_index.are | 100, 167, 174, 220 |
| abstract_inverted_index.can | 244 |
| abstract_inverted_index.few | 56 |
| abstract_inverted_index.for | 53, 96 |
| abstract_inverted_index.our | 105, 242 |
| abstract_inverted_index.the | 39, 61, 78, 135, 148, 181, 226 |
| abstract_inverted_index.two | 30 |
| abstract_inverted_index.99%, | 239 |
| abstract_inverted_index.SFAM | 173 |
| abstract_inverted_index.SSAM | 139 |
| abstract_inverted_index.With | 60 |
| abstract_inverted_index.been | 13 |
| abstract_inverted_index.deep | 164 |
| abstract_inverted_index.fine | 6 |
| abstract_inverted_index.four | 101, 223, 235 |
| abstract_inverted_index.have | 12 |
| abstract_inverted_index.main | 102 |
| abstract_inverted_index.more | 72, 201 |
| abstract_inverted_index.poor | 51, 151 |
| abstract_inverted_index.that | 163, 241 |
| abstract_inverted_index.with | 29, 55 |
| abstract_inverted_index.(HSI) | 19 |
| abstract_inverted_index.CSFEM | 215 |
| abstract_inverted_index.Kappa | 233 |
| abstract_inverted_index.There | 99 |
| abstract_inverted_index.based | 89, 140, 179 |
| abstract_inverted_index.fully | 133 |
| abstract_inverted_index.image | 18 |
| abstract_inverted_index.label | 160 |
| abstract_inverted_index.major | 31 |
| abstract_inverted_index.noisy | 207 |
| abstract_inverted_index.other | 252 |
| abstract_inverted_index.parts | 103 |
| abstract_inverted_index.role. | 24 |
| abstract_inverted_index.CHSFEM | 171 |
| abstract_inverted_index.First, | 131 |
| abstract_inverted_index.above, | 81 |
| abstract_inverted_index.caused | 154 |
| abstract_inverted_index.exceed | 238 |
| abstract_inverted_index.former | 177 |
| abstract_inverted_index.issues | 79 |
| abstract_inverted_index.latter | 191 |
| abstract_inverted_index.method | 243 |
| abstract_inverted_index.module | 110, 117, 122, 129 |
| abstract_inverted_index.neural | 10, 64 |
| abstract_inverted_index.number | 40, 158 |
| abstract_inverted_index.obtain | 200 |
| abstract_inverted_index.public | 224 |
| abstract_inverted_index.widely | 14 |
| abstract_inverted_index.(SFAM), | 123 |
| abstract_inverted_index.(SSAM), | 111 |
| abstract_inverted_index.achieve | 245 |
| abstract_inverted_index.address | 77 |
| abstract_inverted_index.applied | 15 |
| abstract_inverted_index.capture | 185 |
| abstract_inverted_index.complex | 36 |
| abstract_inverted_index.extract | 71 |
| abstract_inverted_index.feature | 87, 115, 127 |
| abstract_inverted_index.indexes | 228 |
| abstract_inverted_index.labeled | 42, 57 |
| abstract_inverted_index.models, | 66 |
| abstract_inverted_index.network | 65, 88 |
| abstract_inverted_index.playing | 21 |
| abstract_inverted_index.problem | 149 |
| abstract_inverted_index.propose | 83 |
| abstract_inverted_index.samples | 43 |
| abstract_inverted_index.shuffle | 91, 108 |
| abstract_inverted_index.solving | 147 |
| abstract_inverted_index.studies | 27 |
| abstract_inverted_index.towards | 205 |
| abstract_inverted_index.(CSFEM). | 130 |
| abstract_inverted_index.Finally, | 204 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.Numerous | 218 |
| abstract_inverted_index.ability, | 8 |
| abstract_inverted_index.abundant | 212 |
| abstract_inverted_index.category | 136 |
| abstract_inverted_index.channels | 198 |
| abstract_inverted_index.compress | 197 |
| abstract_inverted_index.contains | 35 |
| abstract_inverted_index.datasets | 236 |
| abstract_inverted_index.devised. | 217 |
| abstract_inverted_index.excavate | 134 |
| abstract_inverted_index.extract, | 170 |
| abstract_inverted_index.features | 166 |
| abstract_inverted_index.methods. | 254 |
| abstract_inverted_index.multiple | 193 |
| abstract_inverted_index.networks | 11 |
| abstract_inverted_index.previous | 26 |
| abstract_inverted_index.samples. | 161 |
| abstract_inverted_index.samples; | 58 |
| abstract_inverted_index.semantic | 126, 213 |
| abstract_inverted_index.struggle | 28 |
| abstract_inverted_index.superior | 250 |
| abstract_inverted_index.(CHSFEM), | 118 |
| abstract_inverted_index.approach: | 106 |
| abstract_inverted_index.attention | 94, 109, 121 |
| abstract_inverted_index.attribute | 137 |
| abstract_inverted_index.capturing | 211 |
| abstract_inverted_index.datasets: | 225 |
| abstract_inverted_index.deepening | 62 |
| abstract_inverted_index.designed, | 146 |
| abstract_inverted_index.different | 45 |
| abstract_inverted_index.difficult | 69, 168 |
| abstract_inverted_index.features, | 38 |
| abstract_inverted_index.features. | 75, 189, 203 |
| abstract_inverted_index.frequency | 93, 120, 194 |
| abstract_inverted_index.mentioned | 80 |
| abstract_inverted_index.resulting | 49 |
| abstract_inverted_index.structure | 144, 183 |
| abstract_inverted_index.boundaries | 208 |
| abstract_inverted_index.categories | 46, 54 |
| abstract_inverted_index.components | 195 |
| abstract_inverted_index.end-to-end | 2 |
| abstract_inverted_index.evaluation | 227 |
| abstract_inverted_index.extraction | 116, 128 |
| abstract_inverted_index.high-level | 113, 187 |
| abstract_inverted_index.introduces | 192 |
| abstract_inverted_index.mechanisms | 95 |
| abstract_inverted_index.multiscale | 188 |
| abstract_inverted_index.unbalanced | 157 |
| abstract_inverted_index.Considering | 162 |
| abstract_inverted_index.challenges: | 32 |
| abstract_inverted_index.contrasting | 253 |
| abstract_inverted_index.efficiently | 209 |
| abstract_inverted_index.experiments | 219 |
| abstract_inverted_index.implemented | 221 |
| abstract_inverted_index.performance | 153, 247 |
| abstract_inverted_index.suppressing | 206 |
| abstract_inverted_index.topographic | 37 |
| abstract_inverted_index.unbalanced, | 48 |
| abstract_inverted_index.constructed. | 175 |
| abstract_inverted_index.information, | 138, 214 |
| abstract_inverted_index.multifarious | 202 |
| abstract_inverted_index.optimization | 3 |
| abstract_inverted_index.satisfactory | 246 |
| abstract_inverted_index.context-aware | 112, 186 |
| abstract_inverted_index.convolutional | 9 |
| abstract_inverted_index.demonstrating | 240 |
| abstract_inverted_index.hyperspectral | 17 |
| abstract_inverted_index.irreplaceable | 23 |
| abstract_inverted_index.classification | 52, 152 |
| abstract_inverted_index.discriminative | 73, 85 |
| abstract_inverted_index.generalization | 7 |
| abstract_inverted_index.characteristics | 4 |
| abstract_inverted_index.classification, | 20 |
| abstract_inverted_index.classification. | 98 |
| abstract_inverted_index.cross-connected | 125 |
| abstract_inverted_index.spectral-spatial | 74, 107, 114, 119, 165 |
| abstract_inverted_index.spectral-spatial-semantic | 86 |
| abstract_inverted_index.“Horizontal-Vertical” | 182 |
| abstract_inverted_index.“Deconstruction-Reconstruction” | 143 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5088872755 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210088164 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.66407298 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |