{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:39:29Z","timestamp":1762868369605,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant 61772274, Grant 61976117, Grant 61701238, Grant 61501241, Grant 61671243, and Grant 61802190"],"award-info":[{"award-number":["Grant 61772274, Grant 61976117, Grant 61701238, Grant 61501241, Grant 61671243, and Grant 61802190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Jiangsu Provincial Natural Science Foundation of China","award":["Grant BK20180018, Grant BK20170858, and Grant BK20191409"],"award-info":[{"award-number":["Grant BK20180018, Grant BK20170858, and Grant BK20191409"]}]},{"name":"The Fundamental Research Funds for the Central Universities","award":["Grant 30917015104, Grant 30919011103, and Grant 30919011402, Grant 30919011234"],"award-info":[{"award-number":["Grant 30917015104, Grant 30919011103, and Grant 30919011402, Grant 30919011234"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.<\/jats:p>","DOI":"10.3390\/rs13020176","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T20:45:42Z","timestamp":1609965942000},"page":"176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5072-4224","authenticated-orcid":false,"given":"Peng","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7162-0202","authenticated-orcid":false,"given":"Zebin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4855-2499","authenticated-orcid":false,"given":"Jin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yaoqin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yuan","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jiandong","family":"Yang","sequence":"additional","affiliation":[{"name":"China Satellite Maritime Tracking and Control Department, Jiangyin 214431, China"}]},{"given":"Zhihui","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-1659","authenticated-orcid":false,"given":"Antonio","family":"Plaza","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, 10071 C\u00e1ceres, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1109\/TGRS.2003.812908","article-title":"Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping","volume":"41","author":"Kruse","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5123","DOI":"10.1109\/JSTARS.2016.2616514","article-title":"An improved method for mapping tidal flats based on remote sensing waterlines: A case study in the Bohai Rim, China","volume":"9","author":"Liu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, J., Xu, T., Xiao, J., Liu, S., Mao, K., Song, L., Yao, Y., He, X., and Feng, H. (2020). Responses of water use efficiency to drought in southwest China. Remote Sens., 12.","DOI":"10.3390\/rs12010199"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cao, L., Chen, X., Zhang, C., Kurban, A., Qian, J., Pan, T., Yin, Z., Qin, X., Ochege, F.U., and Maeyer, P.D. (2019). The global spatiotemporal distribution of the mid-tropospheric CO2 concentration and analysis of the controlling factors. Remote Sens., 11.","DOI":"10.3390\/rs11010094"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/TGRS.2018.2865429","article-title":"Gaussian process regression for arctic coastal erosion forecasting","volume":"57","author":"Kupilik","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cao, R., Feng, Y., Liu, X., Shen, M., and Zhou, J. (2020). Uncertainty of vegetation green-up date estimated from vegetation indices due to snowmelt at northern middle and high latitudes. Remote Sens., 12.","DOI":"10.3390\/rs12010190"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/LGRS.2017.2687700","article-title":"Application potential of GF-4 images for dynamic ship monitoring","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Arias, L., Cifuentes, J., Mar\u00edn, M., Castillo, F., and Garc\u00e9s, H. (2019). Hyperspectral imaging retrieval using MODIS satellite sensors applied to volcanic ash clouds monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11111393"},{"key":"ref_10","first-page":"497","article-title":"Scale mismatch between in situ and remote sensing observations of land surface temperature: Implications for the validation of remote sensing LST products","volume":"12","author":"Yu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ke, C. (2017, January 27\u201329). Military object detection using multiple information extracted from hyperspectral imagery. Proceedings of the IEEE International Conference on Progress in Informatics and Computing, Nanjing, China.","DOI":"10.1109\/PIC.2017.8359527"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4610","DOI":"10.1109\/JSTARS.2015.2424683","article-title":"Real-time big data analytical architecture for remote sensing application","volume":"8","author":"Rathore","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","first-page":"4601","article-title":"Report of the NOAA panel on contingent valuation","volume":"58","author":"Arrow","year":"1993","journal-title":"Fed. Regist."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/S0034-4257(02)00076-7","article-title":"The MODIS fire products","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/LGRS.2017.2755061","article-title":"Multiscale superpixel-level subspace-based support vector machines for hyperspectral image classification","volume":"14","author":"Yu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1049\/iet-ipr.2018.5458","article-title":"Bilinear normal mixing model for spectral unmixing","volume":"13","author":"Luo","year":"2019","journal-title":"IET Image Processing"},{"key":"ref_18","first-page":"1","article-title":"GPU parallel implementation of spatially adaptive hyperspectral image classification","volume":"4","author":"Wu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zheng, X., Xue, Y., Guang, J., and Liu, J. (2017, January 10\u201315). Remote sensing data processing acceleration based on multi-core processors. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729161"},{"key":"ref_20","unstructured":"Rabe, B.R., Clifford, M., and Miles, N. (2007). Storage Area Network (SAN) Management System for Discovering SAN Components Using a SAN Management Server. (7194538B1), U.S. Patent."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1145\/176979.176981","article-title":"RAID: High-performance, reliable secondary storage","volume":"26","author":"Chen","year":"1994","journal-title":"ACM Comput. Surv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/353360.353362","article-title":"Network attached storage architecture","volume":"43","author":"Gibson","year":"2000","journal-title":"Commun. ACM"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zheng, P., Wu, Z., Zhang, W., Li, M., Yang, J., Zhang, Y., and Wei, Z. (2018, January 23\u201327). An unmixing-based content retrieval method for hyperspectral imagery repository on cloud computing platform. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517591"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4675","DOI":"10.1109\/JSTARS.2015.2426054","article-title":"A first assessment of the P-SBAS DInSAR algorithm performances within a cloud computing environment","volume":"8","author":"Zinno","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/JSTARS.2016.2542193","article-title":"Parallel and distributed dimensionality reduction of hyperspectral data on cloud computing architectures","volume":"9","author":"Wu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cafaro, M., and Giovanni, A. (2011). Grids, clouds, and virtualization. Grids, Clouds and Virtualization, Springer.","DOI":"10.1007\/978-0-85729-049-6"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1145\/2934664","article-title":"Apache spark: A unified engine for big data processing","volume":"59","author":"Zaharia","year":"2016","journal-title":"Commun. ACM"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shvachko, K., Kuang, H., Radia, S., and Chansler, R. (2010, January 3\u20137). The hadoop distributed file system. Proceedings of the IEEE Symposium on Mass Storage Systems and Technologies, Lake Tahoe, NV, USA.","DOI":"10.1109\/MSST.2010.5496972"},{"key":"ref_29","unstructured":"Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., and Goetz, A. (1992, January 1\u20135). The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data. Proceedings of the Third Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1109\/TGRS.2006.890580","article-title":"Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on self-organizing maps","volume":"45","author":"Molinier","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/LGRS.2005.856701","article-title":"A fast iterative algorithm for implementation of pixel purity index","volume":"3","author":"Chang","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","first-page":"29","article-title":"Pixel purity index-based algorithms for endmember extraction from hyperspectral imagery","volume":"37","author":"Chaudhry","year":"2006","journal-title":"Recent Adv. Hyperspectral Signal Image Process."},{"key":"ref_34","unstructured":"Wild, E.C. (2010). USGS library training and outerach: Finding and using scientific literature and data. Geoscience Information Services: \u201cPeak\u201d Performances, Geoscience Information Society."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1086\/322526","article-title":"Multiscale Gaussian random fields and their application to cosmological simulations","volume":"137","author":"Bertschinger","year":"2001","journal-title":"Astrophys. J. Suppl. Ser."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(93)90012-M","article-title":"The airborne visible\/infrared imaging spectrometer (AVIRIS)","volume":"44","author":"Vane","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4294","DOI":"10.1109\/TGRS.2018.2890513","article-title":"An efficient and scalable framework for processing remotely sensed big data in cloud computing environments","volume":"57","author":"Sun","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, Z., Sun, J., Zhang, Y., Zhu, Y., Li, J., Plaza, A., Benediktsson, J.A., and Wei, Z. (2020). Scheduling-guided automatic processing of massive hyperspectral image classification on cloud computing. IEEE Trans. Cybern.","DOI":"10.1109\/TCYB.2020.3026673"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/176\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:07:49Z","timestamp":1760159269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,6]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020176"],"URL":"https:\/\/doi.org\/10.3390\/rs13020176","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,1,6]]}}}