An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification
Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner. In recent years, as a result of the development of computer vision, deep learning techniques have demonstrated their superiority in...
Gespeichert in:
| Veröffentlicht in: | Remote sensing (Basel, Switzerland) Jg. 15; H. 2; S. 451 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.01.2023
|
| Schlagworte: | |
| ISSN: | 2072-4292, 2072-4292 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner. In recent years, as a result of the development of computer vision, deep learning techniques have demonstrated their superiority in tackling the problems of hyperspectral unmixing (HU) and classification. In this paper, we present a new semi-supervised pipeline for few-shot hyperspectral classification, where endmember abundance maps obtained by HU are treated as latent features for classification. A cube-based attention 3D convolutional autoencoder network (CACAE), is applied to extract spectral–spatial features. In addition, an attention approach is used to improve the accuracy of abundance estimation by extracting the diagnostic spectral features associated with the given endmember more effectively. The endmember abundance estimated by the proposed model outperforms other convolutional neural networks (CNNs) with respect to the root mean square error (RMSE) and abundance spectral angle distance (ASAD). Classification experiments are performed on real hyperspectral datasets and compared to several supervised and semi-supervised models. The experimental findings demonstrate that the proposed approach has promising potential for hyperspectral feature extraction and has better performance relative to CNN-based supervised classification under small-sample conditions. |
|---|---|
| AbstractList | Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner. In recent years, as a result of the development of computer vision, deep learning techniques have demonstrated their superiority in tackling the problems of hyperspectral unmixing (HU) and classification. In this paper, we present a new semi-supervised pipeline for few-shot hyperspectral classification, where endmember abundance maps obtained by HU are treated as latent features for classification. A cube-based attention 3D convolutional autoencoder network (CACAE), is applied to extract spectral–spatial features. In addition, an attention approach is used to improve the accuracy of abundance estimation by extracting the diagnostic spectral features associated with the given endmember more effectively. The endmember abundance estimated by the proposed model outperforms other convolutional neural networks (CNNs) with respect to the root mean square error (RMSE) and abundance spectral angle distance (ASAD). Classification experiments are performed on real hyperspectral datasets and compared to several supervised and semi-supervised models. The experimental findings demonstrate that the proposed approach has promising potential for hyperspectral feature extraction and has better performance relative to CNN-based supervised classification under small-sample conditions. |
| Author | Li, Chunyu Yu, Junchuan Cai, Rong |
| Author_xml | – sequence: 1 givenname: Chunyu surname: Li fullname: Li, Chunyu – sequence: 2 givenname: Rong surname: Cai fullname: Cai, Rong – sequence: 3 givenname: Junchuan orcidid: 0000-0003-2987-0504 surname: Yu fullname: Yu, Junchuan |
| BookMark | eNptkd9rFDEQxxepYK196V8Q8EWE1cmP3Wwez9PaQsEH2-eQTSY1x15yJtlq_3v3eoqldF4yDJ_5QL7zujmKKWLTnFH4wLmCj7nQDhiIjr5ojhlI1gqm2NGj_lVzWsoGluKcKhDHjV9FsqoVYw0ptp9MQUf4Z7JO8S5N835oJrKaa8Jok8NMfMrkHH-133-kSi7ud5jLDm3NC3YTt-F3iLfEREfWkykl-GDNXvKmeenNVPD073vS3Jx_uV5ftFffvl6uV1et5UrUljOKDFyHqJyBfjR2EFJRA4NDtGrsO2dkb6TvpB8RpIKxN8p2crDSKxz4SXN58LpkNnqXw9bke51M0A-DlG-1yTXYCbUQcvH2g-eSiZG6YfS-6wU3dBz4INXiendw7XL6OWOpehuKxWkyEdNcNAcBgnEOdEHfPkE3ac5LdEUz2UsO0A17Cg6UzamUjF7bUB_iWeILk6ag93fU_--4rLx_svLvT8_AfwDOEp7R |
| CitedBy_id | crossref_primary_10_1080_01431161_2024_2334772 crossref_primary_10_1016_j_rse_2024_114291 crossref_primary_10_1109_JSTARS_2024_3359647 crossref_primary_10_3390_rs16132449 crossref_primary_10_3389_feart_2023_1229704 crossref_primary_10_3390_rs15112898 crossref_primary_10_1016_j_jfranklin_2023_08_027 |
| Cites_doi | 10.1109/JSTARS.2014.2322143 10.1080/2150704X.2017.1331053 10.1364/AO.403366 10.1109/TGRS.2018.2856929 10.1109/TGRS.2020.3010826 10.1080/01431161.2016.1225173 10.1109/TGRS.2018.2852066 10.3390/rs13132607 10.1109/MGRS.2019.2912563 10.1109/TGRS.2014.2381602 10.1109/36.911111 10.1109/JSTARS.2012.2194696 10.1016/j.ipm.2009.03.002 10.1109/LGRS.2010.2047711 10.1109/TGRS.2008.2001035 10.1109/ACCESS.2018.2818280 10.3390/rs12132106 10.1117/12.366289 10.1109/JSTARS.2020.2966512 10.1109/TGRS.2006.888466 10.1109/MSP.2013.2279274 10.1109/TGRS.2017.2693346 10.1109/TGRS.2019.2908756 10.1109/18.857802 10.1109/TPAMI.2012.59 10.1109/TGRS.2019.2952758 10.1109/TGRS.2011.2160950 10.1109/TGRS.2005.844293 10.1109/TGRS.2020.3004353 10.1109/TGRS.2019.2907932 10.1109/LGRS.2018.2841400 10.1109/TGRS.2010.2098414 10.1016/j.patrec.2005.08.011 10.1109/TIP.2015.2468177 10.1109/TGRS.2019.2892903 10.1109/TGRS.2016.2535298 10.1109/JSTARS.2016.2624560 10.1109/TGRS.2004.831865 10.1109/TGRS.2015.2513082 10.5194/isprs-annals-V-2-2020-203-2020 10.1080/01431169608948706 10.1109/JSTARS.2014.2329330 10.1016/j.neucom.2021.03.035 10.1016/j.patrec.2018.10.003 10.1109/TGRS.2004.841417 10.1109/TGRS.2018.2868690 10.1109/TGRS.2020.2992743 10.1109/LGRS.2018.2857804 10.1109/TGRS.2018.2890633 10.1016/j.rse.2020.111938 |
| ContentType | Journal Article |
| Copyright | 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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/rs15020451 |
| 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 - QC 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 | Publicly Available Content Database CrossRef AGRICOLA |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals (DOAJ) 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_447a0868f3724b1d8bff5643a1b83879 10_3390_rs15020451 |
| 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 |
| ID | FETCH-LOGICAL-c394t-321e20d5ee9da06bac84791a08deec9b65da76a7f57fbe0790b6a9c578c7f9e83 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000918910300001&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 12:38:03 EDT 2025 Sun Nov 09 14:24:55 EST 2025 Sun Jul 13 03:41:21 EDT 2025 Tue Nov 18 21:26:05 EST 2025 Sat Nov 29 07:12:23 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c394t-321e20d5ee9da06bac84791a08deec9b65da76a7f57fbe0790b6a9c578c7f9e83 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-2987-0504 |
| OpenAccessLink | https://www.proquest.com/docview/2767300581?pq-origsite=%requestingapplication% |
| PQID | 2767300581 |
| PQPubID | 2032338 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_447a0868f3724b1d8bff5643a1b83879 proquest_miscellaneous_3040423301 proquest_journals_2767300581 crossref_citationtrail_10_3390_rs15020451 crossref_primary_10_3390_rs15020451 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-01-01 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Remote sensing (Basel, Switzerland) |
| PublicationYear | 2023 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Zhang (ref_16) 2020; 247 Mei (ref_25) 2017; 55 Li (ref_50) 2014; 7 Miao (ref_33) 2007; 45 Zhu (ref_28) 2021; 60 ref_56 Qu (ref_43) 2019; 57 ref_53 Chen (ref_26) 2014; 7 ref_18 Jia (ref_54) 2022; 60 Dobigeon (ref_30) 2014; 31 Zhang (ref_35) 2018; 15 Dobigeon (ref_45) 2015; 24 Guan (ref_29) 2022; 60 Sun (ref_13) 2016; 37 Khajehrayeni (ref_46) 2020; 13 Wambugu (ref_20) 2021; 105 Savas (ref_42) 2019; 57 Su (ref_44) 2018; 15 Licciardi (ref_37) 2011; 49 Jia (ref_11) 2016; 51 Halimi (ref_36) 2011; 49 Melgani (ref_4) 2004; 42 Hecker (ref_2) 2008; 46 Li (ref_10) 2015; 53 Su (ref_40) 2019; 57 Nowakowski (ref_22) 2021; 98 Briechle (ref_23) 2020; 2 Winter (ref_31) 1999; 3753 Ji (ref_51) 2013; 35 Jia (ref_55) 2022; 19 Plaza (ref_1) 2012; 5 Jia (ref_21) 2021; 448 Shang (ref_12) 2021; 59 Zhu (ref_38) 2016; 54 Palsson (ref_41) 2018; 6 Liu (ref_24) 2017; 8 Heinz (ref_57) 2001; 39 Mei (ref_27) 2019; 57 Wang (ref_14) 2021; 59 Sokolova (ref_58) 2009; 45 Benediktsson (ref_6) 1989; 2 Gislason (ref_5) 2006; 27 Tarabalka (ref_9) 2010; 7 Palsson (ref_39) 2021; 59 Li (ref_15) 2020; 58 Chang (ref_3) 2000; 46 Han (ref_47) 2020; 130 Foody (ref_52) 1996; 17 Ma (ref_8) 2010; 48 Nascimento (ref_32) 2005; 43 Li (ref_17) 2019; 57 Plaza (ref_7) 2005; 43 Xu (ref_49) 2017; 10 Gao (ref_34) 2019; 57 Audebert (ref_19) 2019; 7 Drumetz (ref_48) 2019; 57 |
| References_xml | – volume: 7 start-page: 3619 year: 2014 ident: ref_50 article-title: A New Hybrid Strategy Combining Semisupervised Classification and Unmixing of Hyperspectral Data publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2014.2322143 – volume: 8 start-page: 839 year: 2017 ident: ref_24 article-title: A semi-supervised convolutional neural network for hyperspectral image classification publication-title: Remote Sens. Lett. doi: 10.1080/2150704X.2017.1331053 – volume: 60 start-page: A38 year: 2021 ident: ref_28 article-title: Digital holographic imaging and classification of microplastics using deep transfer learning publication-title: Appl. Opt. doi: 10.1364/AO.403366 – volume: 57 start-page: 482 year: 2019 ident: ref_42 article-title: EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2856929 – volume: 59 start-page: 6044 year: 2021 ident: ref_12 article-title: Target-constrained interference-minimized band selection for hyperspectral target detection publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.3010826 – volume: 37 start-page: 4874 year: 2016 ident: ref_13 article-title: A robust and efficient band selection method using graph representation for hyperspectral imagery publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2016.1225173 – volume: 57 start-page: 46 year: 2019 ident: ref_34 article-title: Tensorized principal component alignment: A unified framework for multimodal high-resolution images classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2852066 – ident: ref_53 doi: 10.3390/rs13132607 – volume: 7 start-page: 159 year: 2019 ident: ref_19 article-title: Deep learning for classification of hyperspectral data: A comparative review publication-title: IEEE Geosci. Remote Sens. Mag. doi: 10.1109/MGRS.2019.2912563 – volume: 53 start-page: 3681 year: 2015 ident: ref_10 article-title: Local binary patterns and extreme learning machine for hyperspectral imagery classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2381602 – volume: 39 start-page: 529 year: 2001 ident: ref_57 article-title: Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.911111 – volume: 5 start-page: 354 year: 2012 ident: ref_1 article-title: Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches publication-title: IEEE J. Sel. Top. Appl. Earth Observat. Remote Sens. doi: 10.1109/JSTARS.2012.2194696 – ident: ref_56 – volume: 45 start-page: 427 year: 2009 ident: ref_58 article-title: A systematic analysis of performance measures for classification tasks publication-title: Inf. Process. Manag. doi: 10.1016/j.ipm.2009.03.002 – volume: 7 start-page: 736 year: 2010 ident: ref_9 article-title: SVM-and MRF-based method for accurate classification of hyperspectral images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2010.2047711 – volume: 46 start-page: 4162 year: 2008 ident: ref_2 article-title: Assessing the influence of reference spectra on synthetic SAM classification results publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2008.2001035 – volume: 6 start-page: 25646 year: 2018 ident: ref_41 article-title: Hyperspectral unmixing using a neural network autoencoder publication-title: IEEE Access. doi: 10.1109/ACCESS.2018.2818280 – ident: ref_18 doi: 10.3390/rs12132106 – volume: 3753 start-page: 266 year: 1999 ident: ref_31 article-title: N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data publication-title: In Proc. SPIE. doi: 10.1117/12.366289 – volume: 60 start-page: 1 year: 2022 ident: ref_54 article-title: Tradeoffs in the spatial and spectral resolution of airborne hyperspectral imaging systems: A crop identification case study publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 13 start-page: 567 year: 2020 ident: ref_46 article-title: Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2020.2966512 – volume: 45 start-page: 765 year: 2007 ident: ref_33 article-title: Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.888466 – volume: 48 start-page: 4099 year: 2010 ident: ref_8 article-title: Local manifold learning-based K-nearest-neighbor for hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 31 start-page: 82 year: 2014 ident: ref_30 article-title: Nonlinear unmixing of hyperspectral images: Models and algorithms publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2013.2279274 – volume: 19 start-page: 1 year: 2022 ident: ref_55 article-title: Removing stripe noise based on improved statistics for hyperspectral images publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 55 start-page: 4520 year: 2017 ident: ref_25 article-title: Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2693346 – volume: 57 start-page: 6808 year: 2019 ident: ref_27 article-title: Unsupervised spatial–spectral feature learning by 3D convolutional autoencoder for hyperspectral classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2908756 – volume: 98 start-page: 102313 year: 2021 ident: ref_22 article-title: Crop type mapping by using transfer learning publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 46 start-page: 1927 year: 2000 ident: ref_3 article-title: An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis publication-title: IEEE Trans. Inf. Theory. doi: 10.1109/18.857802 – volume: 2 start-page: 489 year: 1989 ident: ref_6 article-title: Neural network approaches versus statistical methods in classification of multisource remote sensing data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 105 start-page: 102603 year: 2021 ident: ref_20 article-title: Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 35 start-page: 221 year: 2013 ident: ref_51 article-title: 3D convolutional neural networks for human action recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.59 – volume: 58 start-page: 2615 year: 2020 ident: ref_15 article-title: Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2952758 – volume: 49 start-page: 4163 year: 2011 ident: ref_37 article-title: Pixel unmixing in hyperspectral data by means of neural networks publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2160950 – volume: 60 start-page: 1 year: 2022 ident: ref_29 article-title: Cross-domain Contrastive Learning for Hyperspectral Image Classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 43 start-page: 898 year: 2005 ident: ref_32 article-title: Vertex component analysis: A fast algorithm to unmix hyperspectral data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2005.844293 – volume: 59 start-page: 2256 year: 2021 ident: ref_14 article-title: Super-resolution mapping based on spatial-spectral correlation for spectral imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.3004353 – volume: 57 start-page: 6690 year: 2019 ident: ref_17 article-title: Deep Learning for Hyperspectral Image Classification: An Overview publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2907932 – volume: 15 start-page: 1427 year: 2018 ident: ref_44 article-title: Stacked Nonnegative Sparse Autoencoders for Robust Hyperspectral Unmixing publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2018.2841400 – volume: 49 start-page: 4153 year: 2011 ident: ref_36 article-title: Nonlinear unmixing of hyperspectral images using a generalized bilinear model publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2010.2098414 – volume: 27 start-page: 294 year: 2006 ident: ref_5 article-title: Random forests for land cover classification publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2005.08.011 – volume: 24 start-page: 4810 year: 2015 ident: ref_45 article-title: Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2468177 – volume: 57 start-page: 4775 year: 2019 ident: ref_48 article-title: Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2892903 – volume: 54 start-page: 4012 year: 2016 ident: ref_38 article-title: Biobjective nonnegative matrix factorization: Linear versus kernel-based models publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2535298 – volume: 10 start-page: 1589 year: 2017 ident: ref_49 article-title: Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral Imagery: A Quantitative Assessment publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2016.2624560 – volume: 42 start-page: 1778 year: 2004 ident: ref_4 article-title: Classification of hyperspectral remote sensing images with support vector machines publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2004.831865 – volume: 51 start-page: 3174 year: 2016 ident: ref_11 article-title: Gabor cube selection based multitask joint sparse representation for hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2015.2513082 – volume: 2 start-page: 203 year: 2020 ident: ref_23 article-title: Classification of tree species and standing dead trees by fusing UAV-based lidar data and multispectral imagery in the 3D deep neural network PointNet++ publication-title: ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. doi: 10.5194/isprs-annals-V-2-2020-203-2020 – volume: 17 start-page: 1317 year: 1996 ident: ref_52 article-title: Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data publication-title: Int. J. Remote Sens. doi: 10.1080/01431169608948706 – volume: 7 start-page: 2094 year: 2014 ident: ref_26 article-title: Deep learning-based classification of hyperspectral data publication-title: IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2014.2329330 – volume: 448 start-page: 179 year: 2021 ident: ref_21 article-title: A survey: Deep learning for hyperspectral image classification with few labeled samples publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.03.035 – volume: 130 start-page: 38 year: 2020 ident: ref_47 article-title: Joint spatial-spectral hyperspectral image classification based on convolutional neural network publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2018.10.003 – volume: 43 start-page: 466 year: 2005 ident: ref_7 article-title: Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations publication-title: IEEE Trans Geosci. Remote Sens. doi: 10.1109/TGRS.2004.841417 – volume: 57 start-page: 1698 year: 2019 ident: ref_43 article-title: uDAS: An Untied Denoising Autoencoder with Sparsity for Spectral Unmixing publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2868690 – volume: 59 start-page: 535 year: 2021 ident: ref_39 article-title: Convolutional Autoencoder for Spectral-Spatial Hyperspectral Unmixing publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.2992743 – volume: 15 start-page: 1755 year: 2018 ident: ref_35 article-title: Hyperspectral Unmixing via Deep Convolutional Neural Networks publication-title: IEEE Geoence Remote Sensing Lett. doi: 10.1109/LGRS.2018.2857804 – volume: 57 start-page: 4309 year: 2019 ident: ref_40 article-title: DAEN: Deep autoencoder networks for hyperspectral unmixing publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2890633 – volume: 247 start-page: 111938 year: 2020 ident: ref_16 article-title: Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111938 |
| SSID | ssj0000331904 |
| Score | 2.406156 |
| Snippet | Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 451 |
| SubjectTerms | Abundance Accuracy Artificial neural networks autoencoder Classification Computer vision data collection Deep learning Feature extraction few-shot hyperspectral Machine learning Neural networks Remote sensing Root-mean-square errors unmixing |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9wwELUQQoJLRUurbqGVq_bSg0US27F9XCgrTqhSQeIW-WMskNqkymaB_vuOnbBQFYlLr8lEiWbsmTex_R4hn6soQwFlyYLXkQkwJXNWShYKryFWItbOZbEJdXamLy_Nt0dSX2lP2EgPPDruUAhlEXbryFUlXBm0i1FiGbWl01yrfHQPUc-jZirnYI5DqxAjHynHvv6wXyL0Sdzr5V8VKBP1_5OHc3FZ7JIXEyqk8_FrXpINaF-R7Umg_Or3Honzls6HYdybyI6w9ATKv9Ljrr2Zxk56fDV0iZcyQE8Ri9IF3LLvV91AT7HZHM9U9mh20f68vsOKRW0baBbFTNuFcoRek4vFyfnxKZskEpjnRgyMVyVURZAAJtiidtZjtTEleiwAeONqGayqrYpSRQeFMoWrrfE4Tb2KBjR_QzbbroW3hEpZyQqM9BBA8LR8KQRo5aOUPCAwmZEv925r_MQfnmQsfjTYRyQXNw8unpFPa9tfI2vGk1ZHyftri8R0nS9g_Jsp_s1z8Z-Rg_vYNdP0WzaVqjMPv8Z3fFzfxomTVkNsC91q2XBMX4glMcG9-x_fsU92khL9-HfmgGwO_Qreky1_M1wv-w95dP4Bygnpgg priority: 102 providerName: Directory of Open Access Journals |
| Title | An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification |
| URI | https://www.proquest.com/docview/2767300581 https://www.proquest.com/docview/3040423301 https://doaj.org/article/447a0868f3724b1d8bff5643a1b83879 |
| Volume | 15 |
| WOSCitedRecordID | wos000918910300001&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: Directory of Open Access Journals (DOAJ) 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/eLvHCXMwpV09b9QwGLagRYKFb8RBORnBwmA1ie3YntBduVMZOEWUSoUl8mdbCZKS5Aos_HZsx3cVArGweEjeREneT792ngeAl4WjJrN5jozmDhErcqQkpchkmltXEFcqFckm2GrFT05ElRpufdpWuYmJMVCbVoce-X7BygitzvPXF19RYI0Kq6uJQuM62A0oCUXcune07bFk2BtYRkZUUuxn9_td7wuggMCe_5aHIlz_H9E4ppjlnf99uLvgdiou4Wy0hnvgmm3ug5uJ5_zsxwPgZg2cDcO4xRHNfQYzEL-BB21zmUwwXL4e2gBvaWwHfUkLl_YbOjprB3jo56zjr5mdFztuvpx_94kPysbAyK0Zdh1FRT8Ex8vFh4NDlJgWkMaCDAgXuS0yQ60VRmalktonLZHLjBtrtVAlNZKVkjnKnLIZE5kqpdDe2zVzwnL8COw0bWMfA0hpQQsrqLbGEhxWQQmxnGlHKTa-vpmAV5vvXusEQx7YMD7XfjoSdFRf6WgCXmxlL0bwjb9KzYP6thIBMDseaLvTOvlfTQjzb1Nyh1lBVG64co76akzmimPOxATsbTRbJy_u6yu1TsDz7Wnvf2FRRTa2Xfc19lHQl6Q-Tj759y2egluBqn5s3-yBnaFb22fghr4czvtuCnbni1X1fhp7AtNoxmH8ufBjRT_589Xbd9XHX8Tm_vE |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwELVKQSoXvisWChgBBw5RE3_E9gGhbctqq5YVUlupt5DYY1oJkpJkW_qn-I2Mk-xWCMStB67JxJLj5zdjezyPkNfMSxdDkkTOah8JMElU5FJGLrYaPBM-LYpObELNZvr42HxaIT8Xd2FCWuWCEzuidpUNe-SbTKVdaXWdvD_7HgXVqHC6upDQ6GGxB5cXuGRr3u3u4Pi-YWzy4XB7Gg2qApHlRrQRZwmw2EkA4_I4LXKLBG2SPNYOwJoilS5Xaa68VL6AWJm4SHNjEdlWeQOaY7s3yE0R2L9LFTxY7unEHAEdi74KKucm3qwbDLhCxffkN7_XyQP8wf6dS5vc_d9-xj1yZwie6bhH-32yAuUDsjbouJ9cPiR-XNJx2_YpnNEWemhH-Q7drsrzYYqFz-dtFcp3Oqgphux0AhfRwUnV0imuyfurpzWaHZXfTn-gY6d56WinHRqyqjogPyJH19LNdbJaViU8JlRKJhkYacGB4OGUVwjQynopucP4bUTeLsY5s0OZ9aD28TXD5VbARHaFiRF5tbQ964uL_NVqK8BlaREKgncPqvpLNvBLJoTC3qTac8VEkThdeC8x2syTQnOtzIhsLJCUDSzVZFcwGpGXy9fIL-HQKC-hmjcZR5bHkBv9wJN_N_GCrE0PP-5n-7uzvafkNsNgsN-q2iCrbT2HZ-SWPW9Pm_p5N2ko-XzdwPwFK5RXwg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQcCFN2KhgBFw4BBtYsexfUBo22XVqmi1ElSquKSJPaaV2qQk2Zb-NX4d4zy2QiBuPXBNJpYcf56HZzwfIW-YEzaEKAqsUS6IQUdBngkR2NAocCx2SZ63ZBNyPlf7-3qxRn4Od2F8WeWgE1tFbUvjz8jHTCZta3UVjV1fFrGYzj6cfg88g5TPtA50Gh1EduHiHMO3-v3OFNf6LWOzj1-2toOeYSAwXMdNwFkELLQCQNssTPLMoLLWURYqC2B0ngibySSTTkiXQyh1mCeZNohyI50GxXHca-S6xBjTB34L8XV1vhNyBHcYdx1ROdfhuKrR-fLd36PfbGBLFfCHJWjN2-zu__xj7pE7vVNNJ90uuE_WoHhAbvX87ocXD4mbFHTSNF1pZ7CJlttSPqVbZXHWbz3_-bIpfVtPCxVFV57O4Dz4fFg2dBtj9e5KaoVie8XJ0Q80-DQrLG05RX21VQvwR2TvSqb5mKwXZQFPCBWCCQZaGLAQc5_9jWNQ0jghuEW_bkTeDWuemr79umcBOU4xDPP4SC_xMSKvV7KnXdORv0pteuisJHyj8PZBWX1Le72TxrHE2STKccniPLIqd06gF5pFueJK6hHZGFCV9tqrTi8hNSKvVq9R7_hkUlZAuaxTjtofXXG0D0__PcRLchPxmH7ame8-I7cZ-ojdCdYGWW-qJTwnN8xZc1RXL9r9Q8nBVePyF-GRYKU |
| 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=An+Attention-Based+3D+Convolutional+Autoencoder+for+Few-Shot+Hyperspectral+Unmixing+and+Classification&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Li%2C+Chunyu&rft.au=Cai%2C+Rong&rft.au=Yu%2C+Junchuan&rft.date=2023-01-01&rft.issn=2072-4292&rft.eissn=2072-4292&rft.volume=15&rft.issue=2&rft.spage=451&rft_id=info:doi/10.3390%2Frs15020451&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_rs15020451 |
| 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 |