{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T15:25:26Z","timestamp":1775661926439,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T00:00:00Z","timestamp":1713571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research","award":["BIFOLD24B"],"award-info":[{"award-number":["BIFOLD24B"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives. Conventional CBHIR systems characterize each image by a set of endmembers and then perform image retrieval based on pairwise distance measures. Such an approach significantly increases the computational complexity of the retrieval, mainly when the diversity of materials is high. Those systems also have difficulties in retrieving images containing particular materials with extremely low abundance compared to other materials, which leads to describing image content with inappropriate and\/or insufficient spectral features. In this article, a novel CBHIR system to define global hyperspectral image representations based on a semantic approach to differentiate foreground and background image content for different retrieval scenarios is introduced to address these issues. The experiments conducted on a new benchmark archive of multi-label hyperspectral images, which is first introduced in this study, validate the retrieval accuracy and effectiveness of the proposed system. Comparative performance analysis with the state-of-the-art CBHIR systems demonstrates that modeling hyperspectral image content with foreground and background vocabularies has a positive effect on retrieval performance.<\/jats:p>","DOI":"10.3390\/rs16081462","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T03:57:07Z","timestamp":1713758227000},"page":"1462","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8164-8586","authenticated-orcid":false,"given":"Fatih","family":"\u00d6mr\u00fcuzun","sequence":"first","affiliation":[{"name":"Department of Information Systems, Graduate School of Informatics, Middle East Technical University, 06800 Ankara, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5057-668X","authenticated-orcid":false,"given":"Yasemin","family":"Yard\u0131mc\u0131 \u00c7etin","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Graduate School of Informatics, Middle East Technical University, 06800 Ankara, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-7301","authenticated-orcid":false,"given":"U\u011fur Murat","family":"Lelo\u011flu","sequence":"additional","affiliation":[{"name":"Faculty of Aeronautics and Astronautics, Turkish Aeronautical Association University, 06790 Ankara, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2175-7072","authenticated-orcid":false,"given":"Beg\u00fcm","family":"Demir","sequence":"additional","affiliation":[{"name":"BIFOLD\u2014Berlin Institute for the Foundations of Learning and Data, Ernst-Reuter Platz 7, 10587 Berlin, Germany"},{"name":"Faculty of Electrical Engineering and Computer Science, Technische Universit\u00e4t Berlin, 10587 Berlin, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103115","DOI":"10.1016\/j.infrared.2019.103115","article-title":"Status and application of advanced airborne hyperspectral imaging technology: A review","volume":"104","author":"Jia","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"44141","DOI":"10.1007\/s11042-022-13235-x","article-title":"Hyperspectral imaging and target detection algorithms: A review","volume":"81","author":"SSneha","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_5","unstructured":"Pandey, P.C., Srivastava, P.K., Balzter, H., Bhattacharya, B., and Petropoulos, G.P. (2020). Hyperspectral Remote Sensing, Elsevier. Earth Observation."},{"key":"ref_6","unstructured":"Veganzones, M., Datcu, M., and Grana, M. (2012, January 6\u20138). Dictionary based Hyperspectral Image Retrieval. Proceedings of the ICPRAM (1), Vilamoura, Algarve, Portugal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1002\/cpe.1555","article-title":"Parallel heterogeneous CBIR system for efficient hyperspectral image retrieval using spectral mixture analysis","volume":"22","author":"Plaza","year":"2010","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Plaza, A.J. (2011, January 19\u201321). Content-based hyperspectral image retrieval using spectral unmixing. Proceedings of the Image and Signal Processing for Remote Sensing XVII, Prague, Czech Republic.","DOI":"10.1117\/12.898833"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S0218001417520012","article-title":"A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction","volume":"31","author":"Zhang","year":"2017","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zheng, P., Wu, Z., Sun, J., Zhang, Y., Zhu, Y., Shen, Y., Yang, J., Wei, Z., and Plaza, A. (2021). A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms. Remote Sens., 13.","DOI":"10.3390\/rs13020176"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3472","DOI":"10.1016\/j.patcog.2012.03.015","article-title":"An Endmember-Based Distance for Content Based Hyperspectral Image Retrieval","volume":"45","author":"Veganzones","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1109\/JSTARS.2012.2186629","article-title":"A Spectral\/Spatial CBIR System for Hyperspectral Images","volume":"5","author":"Veganzones","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1109\/JSTARS.2014.2314601","article-title":"A New Digital Repository for Hyperspectral Imagery With Unmixing-Based Retrieval Functionality Implemented on GPUs","volume":"7","author":"Sevilla","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1109\/LGRS.2015.2483679","article-title":"Sparse Unmixing-Based Content Retrieval of Hyperspectral Images on Graphics Processing Units","volume":"12","author":"Sevilla","year":"2015","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1108\/SR-10-2014-0716","article-title":"An effective hyperspectral image retrieval method using integrated spectral and textural features","volume":"35","author":"Shao","year":"2015","journal-title":"Sens. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, E., Gong, Y., and Tie, Y. (2016). Advances in Multimedia Information Processing\u2014PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi\u00b4 an, China, 15\u201316 September 2016, Proceedings, Part II, Springer International Publishing.","DOI":"10.1007\/978-3-319-48896-7"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1035021","DOI":"10.1117\/1.JRS.11.035021","article-title":"Secure retrieval method of hyperspectral image in encrypted domain","volume":"11","author":"Zhang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1364\/AO.56.004785","article-title":"Hyperspectral remote sensing image retrieval system using spectral and texture features","volume":"56","author":"Zhang","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"\u00d6mr\u00fcuzun, F., Demir, B., Bruzzone, L., and \u00c7etin, Y.Y. (2016, January 21\u201324). Content based hyperspectral image retrieval using bag of endmembers image descriptors. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071805"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, L., Zhuo, L., Liang, X., and Li, J. (2018). An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing. Remote Sens., 10.","DOI":"10.3390\/rs10020271"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2017.10.020","article-title":"Regional clustering-based spatial preprocessing for hyperspectral unmixing","volume":"204","author":"Xu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_24","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"P Nascimento, J.M., and Bioucas-Dias, J.M. (2007, January 23\u201328). Hyperspectral signal subspace estimation. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423531"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1462\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:31:38Z","timestamp":1760106698000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1462"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,20]]},"references-count":25,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081462"],"URL":"https:\/\/doi.org\/10.3390\/rs16081462","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,20]]}}}