A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification
Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through the use of labeled and unlabeled samples concurrently. However, recent research indicates that the classification performance might be deteriorated by the unlabeled samples. Here, we proposed a novel graph-based...
Gespeichert in:
| Veröffentlicht in: | Remote sensing (Basel, Switzerland) Jg. 13; H. 2; S. 193 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.01.2021
|
| Schlagworte: | |
| ISSN: | 2072-4292, 2072-4292 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through the use of labeled and unlabeled samples concurrently. However, recent research indicates that the classification performance might be deteriorated by the unlabeled samples. Here, we proposed a novel graph-based semi-supervised algorithm combined with particle cooperation and competition, which can improve the model performance effectively by using unlabeled samples. First, for the purpose of reducing the generation of label noise, we used an efficient constrained graph construction approach to calculate the affinity matrix, which is capable of constructing a highly correlated similarity relationship between the graph and the samples. Then, we introduced a particle competition and cooperation mechanism into label propagation, which could detect and re-label misclassified samples dynamically, thus stopping the propagation of wrong labels and allowing the overall model to obtain better classification performance by using predicted labeled samples. Finally, we applied the proposed model into hyperspectral image classification. The experiments used three real hyperspectral datasets to verify and evaluate the performance of our proposal. From the obtained results on three public datasets, our proposal shows great hyperspectral image classification performance when compared to traditional graph-based SSL algorithms. |
|---|---|
| AbstractList | Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through the use of labeled and unlabeled samples concurrently. However, recent research indicates that the classification performance might be deteriorated by the unlabeled samples. Here, we proposed a novel graph-based semi-supervised algorithm combined with particle cooperation and competition, which can improve the model performance effectively by using unlabeled samples. First, for the purpose of reducing the generation of label noise, we used an efficient constrained graph construction approach to calculate the affinity matrix, which is capable of constructing a highly correlated similarity relationship between the graph and the samples. Then, we introduced a particle competition and cooperation mechanism into label propagation, which could detect and re-label misclassified samples dynamically, thus stopping the propagation of wrong labels and allowing the overall model to obtain better classification performance by using predicted labeled samples. Finally, we applied the proposed model into hyperspectral image classification. The experiments used three real hyperspectral datasets to verify and evaluate the performance of our proposal. From the obtained results on three public datasets, our proposal shows great hyperspectral image classification performance when compared to traditional graph-based SSL algorithms. |
| Author | Yin, Zhixian He, Ziping Xia, Kewen Zhang, Jiangnan Li, Tiejun Zu, Baokai |
| Author_xml | – sequence: 1 givenname: Ziping surname: He fullname: He, Ziping – sequence: 2 givenname: Kewen surname: Xia fullname: Xia, Kewen – sequence: 3 givenname: Tiejun surname: Li fullname: Li, Tiejun – sequence: 4 givenname: Baokai surname: Zu fullname: Zu, Baokai – sequence: 5 givenname: Zhixian surname: Yin fullname: Yin, Zhixian – sequence: 6 givenname: Jiangnan surname: Zhang fullname: Zhang, Jiangnan |
| BookMark | eNptkcFq3DAQhkVIoGmaS5_AkEspuJU0km0dt0ubLARaSHoWI1neaLEtV_Km5CH6ztV6U1pCddH8o--fYTSvyekYRkfIW0Y_ACj6MSYGlFOm4IScc1rzUnDFT_-JX5HLlHY0HwCmqDgnv1bFOoxpjuhH1xbXEaeH8hOmHN-5wZd3-8nFR3_Qq34bop8fhuwYzIL_zLL4hnH2tnc5HTKMsw9jgWN7wCY3-0V3IRY3T_k5Tc7mbn2xGXCbPT2m5DtvF9sbctZhn9zl831Bvn_5fL--KW-_Xm_Wq9vSghJzqSqnkFGoamEAG97aqm4rRl1llTPUSgvGSjBSNlXnjKKm4RKEaCwawSoBF2RzrNsG3Okp-gHjkw7o9ZIIcaufh9I1cI4SRcupFI42xjYICloB1EHVqlzr3bHWFMOPvUuzHnyyru9xdGGfNJeMsYZJWmf06gW6C_s45kk1F3XNQFIpM0WPlI0hpeg6bf28fM9hS71mVB_Wrf-uO1vev7D8mek_8G-HCazO |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2023_3328388 crossref_primary_10_1109_JSTARS_2022_3173999 crossref_primary_10_3390_rs15153793 crossref_primary_10_1007_s10462_021_10018_y crossref_primary_10_1007_s10707_021_00443_0 crossref_primary_10_1016_j_eswa_2022_117479 crossref_primary_10_1080_01431161_2022_2048916 crossref_primary_10_1007_s00362_024_01578_6 crossref_primary_10_1117_1_JRS_16_026513 crossref_primary_10_1134_S1054661824010085 crossref_primary_10_1109_JSTARS_2022_3192127 crossref_primary_10_1007_s10462_023_10397_4 crossref_primary_10_1016_j_compag_2023_108577 |
| Cites_doi | 10.3390/rs12061012 10.1609/aaai.v30i1.10302 10.3390/rs12020297 10.1109/JSTARS.2018.2880562 10.1109/SBRN.2012.16 10.1109/JSTARS.2017.2770144 10.1109/JSTARS.2018.2869376 10.3390/rs11242974 10.1109/TGRS.2013.2275613 10.1016/j.patrec.2018.05.001 10.1109/LGRS.2014.2380313 10.1109/JSTARS.2015.2453411 10.1007/s10115-013-0706-y 10.1364/JOSAA.381158 10.1109/TGRS.2018.2884771 10.1109/JSTARS.2018.2809781 10.3390/rs11060654 10.1109/TKDE.2011.119 10.1016/j.patrec.2018.10.001 10.1109/IGARSS.2018.8519132 10.1145/2623330.2623731 10.1109/ICCV.2013.323 10.1109/TGRS.2018.2888485 10.1109/ACCESS.2019.2947742 10.3390/rs12182976 10.1109/JSTARS.2015.2396577 10.3390/rs9040386 10.1109/LGRS.2019.2924059 10.1117/1.JRS.14.024522 10.1109/SSIAI.2018.8470307 10.1016/j.neucom.2014.08.082 10.1109/JSTARS.2018.2873051 10.1109/LGRS.2019.2928009 10.1109/TGRS.2004.842481 10.3390/rs12010159 10.1109/TGRS.2008.916090 10.3390/rs12091528 10.3390/rs10040515 10.1109/TGRS.2018.2838665 10.3390/rs10060817 |
| ContentType | Journal Article |
| Copyright | 2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7S9 L.6 DOA |
| DOI | 10.3390/rs13020193 |
| DatabaseName | CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography |
| EISSN | 2072-4292 |
| ExternalDocumentID | oai_doaj_org_article_7322a5a4d2054e08bc8a393d430e36d9 10_3390_rs13020193 |
| GroupedDBID | 29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQQKQ PQUKI PRINS 7S9 L.6 PUEGO |
| ID | FETCH-LOGICAL-c394t-96e9a103674b3a82dc67d610e6c9eb0c5c3bc53b5586feb90b8253448cab41643 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000611949800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2072-4292 |
| IngestDate | Fri Oct 03 12:40:33 EDT 2025 Fri Sep 05 07:31:45 EDT 2025 Mon Oct 20 02:59:40 EDT 2025 Tue Nov 18 21:41:53 EST 2025 Sat Nov 29 07:15:03 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c394t-96e9a103674b3a82dc67d610e6c9eb0c5c3bc53b5586feb90b8253448cab41643 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/2477135055?pq-origsite=%requestingapplication% |
| PQID | 2477135055 |
| PQPubID | 2032338 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_7322a5a4d2054e08bc8a393d430e36d9 proquest_miscellaneous_2511181507 proquest_journals_2477135055 crossref_citationtrail_10_3390_rs13020193 crossref_primary_10_3390_rs13020193 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-01-01 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – month: 01 year: 2021 text: 2021-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Remote sensing (Basel, Switzerland) |
| PublicationYear | 2021 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | ref_50 Mukherjee (ref_17) 2017; 11 Cao (ref_46) 2020; 14 Breve (ref_26) 2011; 24 Kang (ref_49) 2013; 52 ref_14 Breve (ref_28) 2015; 160 ref_12 ref_11 ref_10 ref_19 Tu (ref_37) 2018; 11 Peerbhay (ref_36) 2015; 8 Zhou (ref_15) 2019; 57 Vanegas (ref_2) 2019; 123 Zhang (ref_35) 2018; 11 ref_25 ref_24 Raveaux (ref_23) 2020; 134 ref_22 ref_20 Fahime (ref_48) 2019; 13 ref_27 Liu (ref_13) 2018; 12 Haut (ref_32) 2018; 56 Triguero (ref_21) 2015; 42 Liu (ref_31) 2018; 11 Ham (ref_34) 2005; 43 Blanzieri (ref_38) 2008; 46 Zhao (ref_47) 2020; 17 ref_39 Mohanty (ref_18) 2019; 57 Gao (ref_30) 2014; 12 Xia (ref_45) 2020; 17 Hoang (ref_9) 2019; 7 Ahmadi (ref_16) 2020; 37 ref_44 ref_43 Priya (ref_33) 2015; 12 ref_42 ref_41 ref_40 ref_1 ref_3 ref_8 ref_5 ref_4 ref_7 ref_6 Tan (ref_29) 2015; 8 |
| References_xml | – ident: ref_14 doi: 10.3390/rs12061012 – ident: ref_5 – ident: ref_25 doi: 10.1609/aaai.v30i1.10302 – ident: ref_40 doi: 10.3390/rs12020297 – volume: 12 start-page: 1 year: 2018 ident: ref_13 article-title: Superpixel-Based Semisupervised Active Learning for Hyperspectral Image Classification publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2018.2880562 – ident: ref_27 doi: 10.1109/SBRN.2012.16 – volume: 11 start-page: 1203 year: 2017 ident: ref_17 article-title: Spatially Constrained Semisupervised Local Angular Discriminant Analysis for Hyperspectral Images publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2017.2770144 – volume: 11 start-page: 4063 year: 2018 ident: ref_37 article-title: Hyperspectral Image Classification via Weighted Joint Nearest Neighbor and Sparse Representation publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2018.2869376 – ident: ref_41 doi: 10.3390/rs11242974 – ident: ref_8 – ident: ref_4 – volume: 52 start-page: 3742 year: 2013 ident: ref_49 article-title: Feature Extraction of Hyperspectral Images with Image Fusion and Recursive Filtering publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2013.2275613 – volume: 134 start-page: 77 year: 2020 ident: ref_23 article-title: Efficient k-nearest neighbors search in graph space publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2018.05.001 – volume: 12 start-page: 1071 year: 2015 ident: ref_33 article-title: Superpixels for Spatially Reinforced Bayesian Classification of Hyperspectral Images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2014.2380313 – volume: 8 start-page: 4647 year: 2015 ident: ref_29 article-title: GPU Parallel Implementation of Support Vector Machines for Hyperspectral Image Classification publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2015.2453411 – volume: 42 start-page: 245 year: 2015 ident: ref_21 article-title: Self-labeled techniques for semi-supervised learning: Taxonomy, software and empirical study publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-013-0706-y – volume: 37 start-page: 606 year: 2020 ident: ref_16 article-title: Semisupervised classification of hyperspectral images with low-rank representation kernel publication-title: J. Opt. Soc. Am. A doi: 10.1364/JOSAA.381158 – volume: 57 start-page: 3423 year: 2019 ident: ref_18 article-title: A Semisupervised Spatial Spectral Regularized Manifold Local Scaling Cut with HGF for Dimensionality Reduction of Hyperspectral Images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2884771 – volume: 11 start-page: 1082 year: 2018 ident: ref_35 article-title: Cascaded Random Forest for Hyperspectral Image Classification publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2018.2809781 – ident: ref_42 doi: 10.3390/rs11060654 – volume: 12 start-page: 349 year: 2014 ident: ref_30 article-title: Subspace-Based Support Vector Machines for Hyperspectral Image Classification publication-title: IEEE Geosci. Remote Sens. Lett. – ident: ref_7 – ident: ref_3 – ident: ref_24 – volume: 24 start-page: 1686 year: 2011 ident: ref_26 article-title: Particle Competition and Cooperation in Networks for Semi-Supervised Learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2011.119 – volume: 123 start-page: 97 year: 2019 ident: ref_2 article-title: Scalable multi-label annotation via semi-supervised kernel semantic embedding publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2018.10.001 – ident: ref_11 doi: 10.1109/IGARSS.2018.8519132 – ident: ref_22 doi: 10.1145/2623330.2623731 – ident: ref_1 doi: 10.1109/ICCV.2013.323 – volume: 57 start-page: 3813 year: 2019 ident: ref_15 article-title: Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image Classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2888485 – volume: 7 start-page: 152187 year: 2019 ident: ref_9 article-title: Detecting mobile traffic anomalies through physical control channel fingerprinting: A deep semi-supervised approach publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2947742 – ident: ref_20 doi: 10.3390/rs12182976 – volume: 8 start-page: 3107 year: 2015 ident: ref_36 article-title: Random Forests Unsupervised Classification: The Detection and Mapping of Solanum mauritianum Infestations in Plantation Forestry Using Hyperspectral Data publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2015.2396577 – ident: ref_44 doi: 10.3390/rs9040386 – ident: ref_6 – volume: 17 start-page: 539 year: 2020 ident: ref_47 article-title: Semisupervised Hyperspectral Image Classification with Cluster-Based Conditional Generative Adversarial Net publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2019.2924059 – ident: ref_50 – volume: 14 start-page: 024522 year: 2020 ident: ref_46 article-title: Semisupervised hyperspectral imagery classification based on a three-dimensional convolutional adversarial autoencoder model with low sample requirements publication-title: J. Appl. Remote Sens. doi: 10.1117/1.JRS.14.024522 – ident: ref_10 doi: 10.1109/SSIAI.2018.8470307 – volume: 160 start-page: 63 year: 2015 ident: ref_28 article-title: Particle competition and cooperation for semi-supervised learning with label noise publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.08.082 – volume: 11 start-page: 4086 year: 2018 ident: ref_31 article-title: Semisupervised Hyperspectral Image Classification via Laplacian Least Squares Support Vector Machine in Sum Space and Random Sampling publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2018.2873051 – volume: 17 start-page: 666 year: 2020 ident: ref_45 article-title: Hyperspectral and LiDAR Classification with Semisupervised Graph Fusion publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2019.2928009 – volume: 43 start-page: 492 year: 2005 ident: ref_34 article-title: Investigation of the random forest framework for classification of hyperspectral data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2004.842481 – ident: ref_19 doi: 10.3390/rs12010159 – volume: 46 start-page: 1804 year: 2008 ident: ref_38 article-title: Nearest Neighbor Classification of Remote Sensing Images with the Maximal Margin Principle publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2008.916090 – ident: ref_39 doi: 10.3390/rs12091528 – ident: ref_43 doi: 10.3390/rs10040515 – volume: 13 start-page: 036512 year: 2019 ident: ref_48 article-title: Improving semisupervised hyperspectral unmixing using spatial correlation under a polynomial postnonlinear mixing model publication-title: J. Appl. Remote Sens. – volume: 56 start-page: 6440 year: 2018 ident: ref_32 article-title: Active Learning with Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2838665 – ident: ref_12 doi: 10.3390/rs10060817 |
| SSID | ssj0000331904 |
| Score | 2.3557563 |
| Snippet | Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through the use of labeled and unlabeled samples concurrently. However, recent... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 193 |
| SubjectTerms | Accuracy Algorithms artificial intelligence Classification Competition Cooperation data collection Datasets exhibitions graph construction hyperspectral image classification hyperspectral imagery Hyperspectral imaging Image classification label propagation learning model validation Noise Noise generation particle competition and cooperation Performance evaluation Propagation Remote sensing sampling Semi-supervised learning sounds Support vector machines |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSyQxEA4ignuRdXVxXJUs68VDY6bTSSfHUXTdiwgqeGvyqF4Fp2eYh-CP8D9blW7HAYW97LHT1ZCuqlTqy6M-xg6ljxj4S48W0D4rTOhnxlid1XTDx5caQNWJbKK8vDR3d_ZqieqLzoS15YFbxR2X6HFOuSLmmFyAMD4YJ62MhRQgdUxX90Rpl8BUisESXUsUbT1Sibj-eDKlLTqRdpiXZqBUqP9DHE6Ty_lXttFlhXzQ9maTrUDzja13BOX3z1vsZcCJWjMROkDkv6k5O8EZKPJrGD5k1_MxDXp6Hjz-HSHivx_iF0OfxGmxlV91_4rNozG0hueuiSQ2hrZmEccUll8gNG1vYE6wT3-GGHF44s6kU0Xps212e352c3qRdUwKWZC2mGVWg3V9nKzKwktn8hh0GTFxAh0seBFUkD4o6ZUyugZvhUfgKBG5BecxYyvkd7bajBrYYZz4v3Vdg5LCIHRRxuVoVGWgHxHOWd9jR2_arUJXZpyU81gh3CBLVO-W6LFfC9lxW1zjU6kTMtJCggpipwZ0k6pTXfUvN-mxvTcTV90onVZ5URJDoVCqx34uXuP4ok0T18BojjKYkWIWhGnz7v_oxw_2JadTMWkRZ4-tziZz2Gdr4Wn2MJ0cJCd-BXcz9mU priority: 102 providerName: Directory of Open Access Journals |
| Title | A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification |
| URI | https://www.proquest.com/docview/2477135055 https://www.proquest.com/docview/2511181507 https://doaj.org/article/7322a5a4d2054e08bc8a393d430e36d9 |
| Volume | 13 |
| WOSCitedRecordID | wos000611949800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: P5Z dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PCBAR dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagRYIL5VWxtKyM4MIhajaOHfuEdqst7YFVxIJUuETxI22lbhKyu0hc-Af858443i0SiAuXSLHHieUZj2fG9nyEvGHaguLPNHBA6CiVZhRJqURU4Q0fnQnneOXBJrLZTJ6fqzwE3JbhWOVGJ3pFbRuDMfKjJM0QTS7m_F37LULUKNxdDRAad8kuZioDOd-dTGf5x22UJWYgYnHa5yVl4N8fdUvcqov9TvNvK5FP2P-HPvaLzMne_3bvEXkYzEs67uXhMbnj6ifkfkA6v_zxlPwaU8To9MgQztL3WBxNYCmzdO4WV9F83aL2wPfx9QX8YXW5gBYL7ckxakvzIG1Q3LSulyBa1hbJWtcnP6JgC9NT8HH7q5wd9OlsAaqLehBOPJ7kmz0jn0-mn45PowDJEBmm0lWkhFPlCFa9LNWslIk1IrNggTlhlNOx4YZpw5nmXIrKaRVr8EAZuICm1GD6pWyf7NRN7Z4TikDioqocZ7EEH4jLMgHp4NKNLPiFSg_I2w17ChPylePgXBfgtyAri1tWDsjrLW3bZ-n4K9UEubylwMzavqDpLoowdEUGGq7kZWoTMGZdLLWRJVPMpix2TFg1IIcbASjCdF8Wt9wfkFfbapiouPtS1q5ZAw2YtmBOgf394t-fOCAPEjw44-M8h2Rn1a3dS3LPfF9dLbthkPChDx4M8ajqHJ8_p_DM-Veoz88-5F9uAA-fDFY |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFKlceCMCBRYBBw5WHa93vXtAKIGWRC1RRIvUm_E-3FZqEpMHqD-Cv8JvZGbtpEggbj1w9Hps2bvffjuzj_kAXnLjkPgzgy0gTZQq24mU0jIq6YSPyaT3ogxiE9lwqI6P9WgDfq7OwtC2yhUnBqJ2U0tz5DtJmpGaXCzE2-prRKpRtLq6ktCoYbHvL75jyDZ_M3iP7fsqSfZ2j971o0ZVILJcp4tIS6-LDhJ3lhpeqMRZmTl0Iry02pvYCsuNFdwIoWTpjY4NBlEcoxhbGPReUo7vvQabKU-laMFmb3c4-rSe1Yk5QjpO6zyonOt4ZzanpcE4rGz_NvIFgYA_-D8Manu3_rfquA03G_eZdWu834ENP7kLW42S--nFPfjRZaRBGpQvvGMfqDjq4VDt2KEfn0WHy4rYka675yf4R4vTMT4xNsGcZqXZqOlNWDytfN1DWDFxZFb5OrkTQ1-f9TGGr4-qzvCbBmOkZhZERmn7VXjsPny-ktp4AK3JdOIfAiOhdFmWXvBYYYwnVJEg-oXyHYdxrzZteL2CQ26bfOxUOec5xmUEnfwSOm14sbat6iwkf7XqEarWFpQ5PBRMZyd5U3V5hgxeiCJ1CTrrPlbGqoJr7lIeey6dbsP2CnB5Q2fz_BJtbXi-vo1ERKtLxcRPl2iDrju6ixhfPPr3K57BVv_o40F-MBjuP4YbCW0SCnNa29BazJb-CVy33xZn89nTpncx-HLVCP4FvbVing |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFAEX3ohAgUXAgYMVx-u1dw8IJbShUVEUUZB6M96H20qNY_IA9Ufwh_h1zKztFAnErQeOXo-tZP3N7Hz7mA_gJdcWA3-q8QskOoil6QdSqiQo6ISPThPnROHFJtLJRB4dqekW_GzPwtC2yjYm-kBt54bmyHtRnJKaXChEr2i2RUx3R2-rrwEpSNFKayunUUPkwJ1_R_q2fDPexW_9KopGe5_e7QeNwkBguIpXgUqcyvsYxNNY81xG1iSpxYTCJUY5HRphuDaCayFkUjitQo2EiiOjMbnGTCbm-N4rsJ1yJD0d2B7uTaYfNzM8IUd4h3FdE5VzFfYWS1omDP0q92-joBcL-GMs8APc6Nb_3DW34WaTVrNB7Qd3YMuVd-F6o_B-cn4PfgwYaZN6RQxn2XtqDoY4hFt26GanweG6oqhJ14OzY_xHq5MZPjHT3pxmq9m08TJsnleu9hyWl5bMKlcXfWLIAdg-cvv6COsCf9N4hiGbefFR2pblH7sPny-lNx5Ap5yX7iEwElBPisIJHkrkfkLmEXqFkK5vkQ8r3YXXLTQy09Rpp845y5CvEYyyCxh14cXGtqqrk_zVakgI21hQRXHfMF8cZ03XZSlG9lzksY0wiXeh1EbmXHEb89DxxKou7LTgy5owt8wukNeF55vbGKBo1Skv3XyNNpjSYxqJvOPRv1_xDK4hbLMP48nBY7gR0d4hP9W1A53VYu2ewFXzbXW6XDxtHI3Bl8sG8C9uWWth |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Constrained+Graph-Based+Semi-Supervised+Algorithm+Combined+with+Particle+Cooperation+and+Competition+for+Hyperspectral+Image+Classification&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.date=2021-01-01&rft.pub=MDPI+AG&rft.eissn=2072-4292&rft.volume=13&rft.issue=2&rft.spage=193&rft_id=info:doi/10.3390%2Frs13020193&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon |