{'fraction_non_alphanumeric': 0.026016260162601626, 'fraction_numerical': 0.0, 'mean_word_length': 6.034285714285715, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Gradient-Based Joint-Variables Nonnegative Matrix Factorization for Multi-Sharpening Hyperspectral Remote Sensing Data

This paper presents a fusion method generating unobservable sharpened hyperspectral remote sensing data with high spatial and spectral resolutions. This method, related to linear spectral unmixing (LSU) techniques, and based on nonnegative matrix factorization (NMF), introduces Joint-Variables NMF (JVNMF) for fusing observable low spatial resolution hyperspectral and high spatial resolution multispectral data. It optimizes a joint-variables criterion that exploits spatial and spectral degradation models between the two considered images, and therefore considers a reduced number of unknown variables. This approach, called Grd-JVNMF, is a gradient-based method and uses iterative update rules. The proposed method is applied to realistic synthetic and semi-real data, and its effectiveness, in spatial and spectral domains, is evaluated with established performance criteria. Experimental results show that the proposed Grd-JVNMF method yields multi-sharpened hyperspectral data with good spectral and spatial fidelities. These tests also show that the proposed method outperforms tested literature ones.