{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:16:48Z","timestamp":1761581808308,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T00:00:00Z","timestamp":1552348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI) produced by different types of imaging sensors, analyzing and retrieving these images require effective image description and quantification techniques. Compared to remote sensing RGB images, HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowing profile materials and organisms that only hyperspectral sensors can provide. In this article, we study the importance of spectral sensitivity functions in constructing discriminative representation of hyperspectral images. The main goal of such representation is to improve image content recognition by focusing the processing on only the most relevant spectral channels. The underlying hypothesis is that for a given category, the content of each image is better extracted through a specific set of spectral sensitivity functions. Those spectral sensitivity functions are evaluated in a Content-Based Image Retrieval (CBIR) framework. In this work, we propose a new HSI dataset for the remote sensing community, specifically designed for Hyperspectral remote sensing retrieval and classification. Exhaustive experiments have been conducted on this dataset and on a literature dataset. Obtained retrieval results prove that the physical measurements and optical properties of the scene contained in the HSI contribute in an accurate image content description than the information provided by the RGB image presentation.<\/jats:p>","DOI":"10.3390\/rs11050600","type":"journal-article","created":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T04:07:37Z","timestamp":1552450057000},"page":"600","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Toward Content-Based Hyperspectral Remote Sensing Image Retrieval (CB-HRSIR): A Preliminary Study Based on Spectral Sensitivity Functions"],"prefix":"10.3390","volume":"11","author":[{"given":"Olfa","family":"Ben-Ahmed","sequence":"first","affiliation":[{"name":"University of Poitiers, CNRS, XLIM, UMR 7252, F-86000 Poitiers, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1339-1920","authenticated-orcid":false,"given":"Thierry","family":"Urruty","sequence":"additional","affiliation":[{"name":"University of Poitiers, CNRS, XLIM, UMR 7252, F-86000 Poitiers, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6891-0540","authenticated-orcid":false,"given":"No\u00ebl","family":"Richard","sequence":"additional","affiliation":[{"name":"University of Poitiers, CNRS, XLIM, UMR 7252, F-86000 Poitiers, France"}]},{"given":"Christine","family":"Fernandez-Maloigne","sequence":"additional","affiliation":[{"name":"University of Poitiers, CNRS, XLIM, UMR 7252, F-86000 Poitiers, France"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bosilj, P., Aptoula, E., Lef\u00e8vre, S., and Kijak, E. (2016). Retrieval of Remote Sensing Images with Pattern Spectra Descriptors. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5120228"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Ma, C., Xia, W., Chen, F., Liu, J., Dai, Q., Jiang, L., Duan, J., and Liu, W. (2017). A Content-Based Remote Sensing Image Change Information Retrieval Model. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.20944\/preprints201708.0102.v1"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, Y., Tao, C., and Zhu, H. (2016). Content-based high-resolution remote sensing image retrieval via unsupervised feature learning and collaborative affinity metric fusion. Remote Sens., 8.","DOI":"10.3390\/rs8090709"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1109\/TGRS.2014.2358804","article-title":"A novel active learning method in relevance feedback for content-based remote sensing image retrieval","volume":"53","author":"Demir","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big data for remote sensing: Challenges and opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/TGRS.2015.2469138","article-title":"Hashing-based scalable remote sensing image search and retrieval in large archives","volume":"54","author":"Demir","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chang, C.I. (2007). Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons.","DOI":"10.1002\/0470124628"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0034-4257(00)00170-X","article-title":"Variations in reflectance of tropical soils: Spectral-chemical composition relationships from AVIRIS data","volume":"75","author":"Pizarro","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, W., Newsam, S., Li, C., and Shao, Z. (arXiv, 2017). PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval, arXiv.","DOI":"10.1016\/j.isprsjprs.2018.01.004"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","article-title":"Medical hyperspectral imaging: A review","volume":"19","author":"Lu","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s00339-011-6689-1","article-title":"Advances in multispectral and hyperspectral imaging for archaeology and art conservation","volume":"106","author":"Liang","year":"2012","journal-title":"Appl. Phys. A"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Barrett, E.C. (2013). Introduction to Environmental Remote Sensing, Routledge.","DOI":"10.4324\/9780203761038"},{"key":"ref_14","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_15","unstructured":"Xia, G., Tong, X., Hu, F., Zhong, Y., Datcu, M., and Zhang, L. (arXiv, 2017). Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation, arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, W., Newsam, S., Li, C., and Shao, Z. (2017). Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sens., 9.","DOI":"10.3390\/rs9050489"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Roy, S., Sangineto, E., Demir, B., and Sebe, N. (2018, January 22\u201327). Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518381"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TGRS.2018.2839705","article-title":"Learning source-invariant deep hashing convolutional neural networks for cross-source remote sensing image retrieval","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TGRS.2017.2756911","article-title":"Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1109\/TGRS.2017.2705073","article-title":"BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification","volume":"55","author":"Santara","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4520","DOI":"10.1109\/TGRS.2017.2693346","article-title":"Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks","volume":"55","author":"Mei","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1242\/jeb.201.16.2343","article-title":"The diversity and implications of animal structural colours","volume":"201","author":"Parker","year":"1998","journal-title":"J. Exp. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0925-2312(02)00698-7","article-title":"Artificial color","volume":"51","author":"Caulfield","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"043514","DOI":"10.1117\/1.3374451","article-title":"Hyperspectral image analysis using artificial color","volume":"4","author":"Fu","year":"2010","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv."},{"key":"ref_30","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TGRS.2004.839547","article-title":"Learning Bayesian classifiers for scene classification with a visual grammar","volume":"43","author":"Aksoy","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1109\/TGRS.2013.2268736","article-title":"Remote sensing image retrieval with global morphological texture descriptors","volume":"52","author":"Aptoula","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.patcog.2006.04.045","article-title":"A survey of content-based image retrieval with high-level semantics","volume":"40","author":"Liu","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"81800O","DOI":"10.1117\/12.898833","article-title":"Content-based hyperspectral image retrieval using spectral unmixing","volume":"8180","author":"Plaza","year":"2011","journal-title":"Proc. SPIE"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Plaza, A., Plaza, J., Paz, A., and Blazquez, S. (2007, January 26\u201329). Parallel CBIR System for Efficient Hyperspectral Image Retrieval from Heterogeneous Networks of Workstations. Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007), Timisoara, Romania.","DOI":"10.1109\/SYNASC.2007.77"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/j.patrec.2013.05.025","article-title":"Further results on dissimilarity spaces for hyperspectral images RF-CBIR","volume":"34","author":"Veganzones","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","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 Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Los Angeles, CA, USA.","DOI":"10.1109\/SIU.2016.7496027"},{"key":"ref_39","first-page":"56","article-title":"Retrieval of multi-and hyperspectral images using an interactive relevance feedback form of content-based image retrieval","volume":"Volume 4384","author":"Alber","year":"2001","journal-title":"Data Mining and Knowledge Discovery: Theory, Tools, and Technology III"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tekeste, I., and Demir, B. (2018, January 22\u201327). Advanced Local Binary Patterns for Remote Sensing Image Retrieval. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518856"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going Deeper With Contextual CNN for Hyperspectral Image Classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spatial Classification of Hyperspectral Data Based on Deep Belief Network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/36.934070","article-title":"Best-bases feature extraction algorithms for classification of hyperspectral data","volume":"39","author":"Kumar","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/TGRS.2017.2769673","article-title":"Supervised Deep Feature Extraction for Hyperspectral Image Classification","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"50402","DOI":"10.2352\/J.ImagingSci.Technol.2016.60.5.050402","article-title":"Pseudo-Divergence and Bidimensional Histogram of Spectral Differences for Hyperspectral Image Processing","volume":"60","author":"Richard","year":"2016","journal-title":"J. Imaging Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"K\u00f6hler, R. (2009). The International Vocabulary of Metrology: Basic and General Concepts and Associated Terms. Why? How?. Transverse Disciplines in Metrology, Wiley & Sons.","DOI":"10.1002\/9780470611371.ch21"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"013201","DOI":"10.1117\/1.2159480","article-title":"Optimization of spectral sensitivities with Gaussian distribution functions for a color image acquisition device in the presence of noise","volume":"45","author":"Shimano","year":"2006","journal-title":"Opt. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/600\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:38:17Z","timestamp":1760186297000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/600"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,12]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["rs11050600"],"URL":"https:\/\/doi.org\/10.3390\/rs11050600","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,3,12]]}}}