Remote Sens., Volume 16, Issue 12 (June-2 2024) – 208 articles
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This study addresses the data gap in the deep learning-based pan sharpening of hyperspectral images, a technique used to improve the spatial resolution of an image using a high-resolution panchromatic image while preserving spectral information. Using the ASI PRISMA sensor, a dataset of 262,200 km2 was collected, making it the largest dataset in terms of statistical relevance and scene diversity, which are essential for robust model generalization. Reduced resolution (RR) and full resolution (FR) experiments were also conducted to compare several deep learning pan sharpening algorithms with various non-machine learning methods. The investigation shows that data-driven neural networks significantly outperform traditional methods in terms of spectral and spatial fidelity. An in-depth analysis of both aspects is presented in this work. View this paper
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