{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:51:30Z","timestamp":1772301090891,"version":"3.50.1"},"reference-count":17,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agtech Growth Fund (AGF) of Innovation Saskatchewan, Co. Labs"},{"name":"Canadian Agri-food Automation and Intelligence Network (CAAIN)"},{"name":"Mitacs"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.<\/jats:p>","DOI":"10.3390\/rs14225774","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:36:36Z","timestamp":1668566196000},"page":"5774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8581-3374","authenticated-orcid":false,"given":"Riyaaz Uddien","family":"Shaik","sequence":"first","affiliation":[{"name":"Super GeoAI Technology Inc., 229-116 Research Drive, Saskatoon, SK S7N 3R3, Canada"}]},{"given":"Aiswarya","family":"Unni","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, University of Rome \u2018La Sapienza\u2019, Via Eudossiana 18, 00184 Rome, Italy"}]},{"given":"Weiping","family":"Zeng","sequence":"additional","affiliation":[{"name":"Super GeoAI Technology Inc., 229-116 Research Drive, Saskatoon, SK S7N 3R3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","unstructured":"Gewali, U.B., Monteiro, S.T., and Saber, E. 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