The method developed here mainly consists in addressing the spectral variability phenomenon in the hypersharpening process, by using the NMF-based unmix- ing algorithm, called inertia-constrained pixel-by-pixel NMF (IP-NMF), which addresses the considered issue when unmixing manipulated data [11].
Apr 22, 2021 · Hypersharpening consists in generating an unobservable high-spatial-resolution hyperspectral image by fusing an observed ...
Oct 22, 2024 · Hypersharpening consists in generating an unobservable high-spatial-resolution hyperspectral image by fusing an observed ...
Hypersharpening consists in generating an unobservable high-spatial-resolution hyperspectral image by fusing an observed low-spatial-resolution ...
In this work, a hypersharpening approach, creating fused hyperspectral remote sensing images with high spatial and spectral resolutions, is introduced.
Experimental results show that the proposed gradient-based joint-criterion NMF (Grd-JCNMF) methods significantly outperform the well-known coupled NMF ...
Feb 20, 2023 · In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the ...
These two unmixing algorithms based on partial NMF, specifically designed to address intra-class variability, can be applied for automated detection, precise ...
Missing: Hypersharpening | Show results with:Hypersharpening
Oct 10, 2024 · Addressing this challenge, this paper introduces an innovative sparse unmixing approach for hyperspectral images named spectral weighted sparse ...
An NMF-based method for jointly handling mixture nonlinearity and intraclass variability in hyperspectral blind source separation · Abstract · Introduction.