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Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation
TLDR
This paper presents a variational-based approach for fusing hyperspectral and multispectral images and demonstrates the efficiency of the proposed algorithm when compared with state-of-the-art fusion methods.
Hyperspectral Pansharpening: A Review
TLDR
This work compares new pansharpening techniques designed for hyperspectral data with some of the state-of-the-art methods for multispectral panshARPening, which have been adapted for hypersittral data.
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
TLDR
This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-sp spatial high-spectrals resolution image, and exploits the properties of the circulant and downsampling matrices associated with the fusion problem.
An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis
TLDR
This paper studies the statistical behavior of an affine combination of the outputs of two least-mean-square adaptive filters that simultaneously adapt using the same white Gaussian inputs to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD).
Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated
Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR
TLDR
The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators.
Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model
TLDR
A new linear mixing model is introduced that explicitly accounts for spatial and spectral endmember variabilities and can be estimated using an optimization algorithm based on the alternating direction method of multipliers.
Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model
TLDR
A generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images and a Metropolis-within-Gibbs algorithm is proposed, which allows samples distributed according to this posterior to be generated and to estimate the unknown model parameters.
Multiband Image Fusion Based on Spectral Unmixing
TLDR
Simulation results show that the proposed unmixing-based fusion scheme improves both the abundance and endmember estimation compared with the state-of-the-art joint fusion and un Mixing algorithms.
Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms
TLDR
This article presents an overview of recent advances in nonlinear unmixing modeling and proposes several significant contributions to overcome the limitations inherent in the LMM.
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