Spatial-Spectral Twin Autoencoders for Hyperspectral Unmixing Via Superpixel-Hypergraph-Augmented Feature Representation
Author:
Affiliation:
1. Donghua University,School of Computer Science and Technology,Shanghai,China
Funder
Natural Science Foundation of Shanghai
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642904.pdf?arnumber=10642904
Reference14 articles.
1. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
2. Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods
3. Vertex component analysis: a fast algorithm to unmix hyperspectral data
4. Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization
5. Hypergraph-Regularized Sparse NMF for Hyperspectral Unmixing
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