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Found 7 papers, 1 papers with code
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
This paper proposes a robust fast multi-band image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image.
Multi-Band Image Fusion Based on Spectral Unmixing
The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem.
Fast Spectral Unmixing based on Dykstra's Alternating Projection
This paper presents a fast spectral unmixing algorithm based on Dykstra's alternating projection.
Robust hyperspectral image classification with rejection fields
We validate our method in real hyperspectral data and show that the performance gains obtained from the rejection fields are equivalent to an increase the dimension of the training sets.
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization
We present a supervised hyperspectral image segmentation algorithm based on a convex formulation of a marginal maximum a posteriori segmentation with hidden fields and structure tensor regularization: Segmentation via the Constraint Split Augmented Lagrangian Shrinkage by Structure Tensor Regularization (SegSALSA-STR).
Performance measures for classification systems with rejection
Classifiers with rejection are essential in real-world applications where misclassifications and their effects are critical.
A Variable Splitting Augmented Lagrangian Approach to Linear Spectral Unmixing
This is a nonconvex optimization problem with convex constraints.
Optimization and Control 46N10
