{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:09:47Z","timestamp":1773252587436,"version":"3.50.1"},"reference-count":12,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T00:00:00Z","timestamp":1571961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The Special Issue on hyperspectral imaging (HSI), entitled \u201cThe Future of Hyperspectral Imaging\u201d, has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI. Some contributions also update specific methodological or mathematical tools. In particular, the review papers address deep learning methods for HSI analysis, while HSI data compression is reviewed by using liquid crystals spectral multiplexing as well as DMD-based Raman spectroscopy. Specific topics explored by using data obtained by HSI include alert on the sprouting of potato tubers, the investigation on the stability of painting samples, the prediction of healing diabetic foot ulcers, and age determination of blood-stained fingerprints. Papers showing advances on more general topics include video approach for HSI dynamic scenes, localization of plant diseases, new methods for the lossless compression of HSI data, the fusing of multiple multiband images, and mixed modes of laser HSI imaging for sorting and quality controls.<\/jats:p>","DOI":"10.3390\/jimaging5110084","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T04:41:27Z","timestamp":1571978487000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["The Future of Hyperspectral Imaging"],"prefix":"10.3390","volume":"5","author":[{"given":"Stefano","family":"Selci","sequence":"first","affiliation":[{"name":"Institute for Photonics and Nanotechnologies, ARTOV C.N.R., Via del Fosso del Cavaliere 100, 00133 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rady, A., Guyer, D., Kirk, W., and Donis-Gonz\u00e1lez, I.R. (2019). Prediction of the Leaf Primordia of Potato Tubers Using Sensor Fusion and Wavelength Selection. J. Imaging, 5.","DOI":"10.3390\/jimaging5010010"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bonifazi, G., Capobianco, G., Pelosi, C., and Serranti, S. (2019). Hyperspectral Imaging as Powerful Technique for Investigating the Stability of Painting Samples. J. Imaging, 5.","DOI":"10.3390\/jimaging5010008"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bachmann, C.M., Eon, R.S., Lapszynski, C.S., Badura, G.P., Vodacek, A., Hoffman, M.J., McKeown, D., Kremens, R.L., Richardson, M., and Bauch, T. (2019). A Low-Rate Video Approach to Hyperspectral Imaging of Dynamic Scenes. J. Imaging, 5.","DOI":"10.3390\/jimaging5010006"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, Q., Sun, S., Jeffcoate, W.J., Clark, D.J., Musgove, A., Game, F.L., and Morgan, S.P. (2018). Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers. J. Imaging, 4.","DOI":"10.3390\/jimaging4120144"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Behmann, J., Bohnenkamp, D., Paulus, S., and Mahlein, A.-K. (2018). Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms. J. Imaging, 4.","DOI":"10.3390\/jimaging4120143"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shen, H., Jiang, Z., and Pan, W.D. (2018). Efficient Lossless Compression of Multitemporal Hyperspectral Image Data. J. Imaging, 4.","DOI":"10.3390\/jimaging4120142"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cadd, S., Li, B., Beveridge, P., O\u2019Hare, W.T., and Islam, M. (2018). Age Determination of Blood-Stained Fingerprints Using Visible Wavelength Reflectance Hyperspectral Imaging. J. Imaging, 4.","DOI":"10.3390\/jimaging4120141"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Arablouei, R. (2018). Fusing Multiple Multiband Images. J. Imaging, 4.","DOI":"10.3390\/jimaging4100118"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gruber, F., Wollmann, P., Gr\u00e4hlert, W., and Kaskel, S. (2018). Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications. J. Imaging, 4.","DOI":"10.3390\/jimaging4100110"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review. J. Imaging, 5.","DOI":"10.3390\/jimaging5050052"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Oiknine, Y., August, I., Farber, V., Gedalin, D., and Stern, A. (2019). Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal. J. Imaging, 5.","DOI":"10.3390\/jimaging5010003"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cebeci, D., Mankani, B.R., and Ben-Amotz, D. (2019). Recent Trends in Compressive Raman Spectroscopy Using DMD-Based Binary Detection. J. Imaging, 5.","DOI":"10.3390\/jimaging5010001"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/5\/11\/84\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:29:15Z","timestamp":1760189355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/5\/11\/84"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,25]]},"references-count":12,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["jimaging5110084"],"URL":"https:\/\/doi.org\/10.3390\/jimaging5110084","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,25]]}}}