Hyperspectral imagery plays a vital role in precision agriculture, forestry, environment, and geological applications. Over the past decade, extensive research has been carried out in the field of hyperspectral remote sensing. First introduced by the Italian Space Agency ASI in 2019, space-borne PRISMA hyperspectral imagery (PHSI) is taking the hyperspectral remote sensing research community into the next era due to its unprecedented spectral resolution of ≤12 nm. Given these abundant free data and high spatial resolution, it is crucial to provide remote sensing researchers with information about the critical attributes of PRISMA imagery, making it the most viable solution for various land and water applications. Hence, in the present study, a SWOT analysis was performed for PHSI using recent case studies to exploit the potential of PHSI for different remote sensing applications, such as snow, soil, water, natural gas, and vegetation. From this analysis, it was found that the higher reflectance spectra of PHSI, which have comprehensive coverage, have greater potential to extract vegetation biophysical parameters compared to other applications. Though the possible use of these data was demonstrated in a few other applications, such as the identification of methane gases and soil mineral mapping, the data may not be suitable for continuous monitoring due to their limited acquisition, long revisiting times, noisy bands, atmospheric interferences, and computationally heavy processing, particularly when executing machine learning models. The potential applications of PHSI include large-scale and efficient mapping, transferring technology, and fusion with other remote sensing data, whereas the lifetime of satellites and the need for interdisciplinary personnel pose challenges. Furthermore, some strategies to overcome the aforementioned weaknesses and threats are described in our conclusions.

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.