Fast Hyperspectral Unmixing Using a Multiscale Sparse Regularization
Author:
Affiliation:
1. Department of Electrical and Electronics Engineering, Gaziantep University, Gaziantep, Turkey
2. IRIT/INP-ENSEEIHT, University of Toulouse, Toulouse, France
Funder
ANR-3IA Artificial and Natural Intelligence Toulouse Institute
French National Research Agency Active Molecular Imaging and Unmixing (ANR IMAGIN) Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology
Link
http://xplorestaging.ieee.org/ielx7/8859/9651998/09931685.pdf?arnumber=9931685
Reference23 articles.
1. Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation
2. Spectral–Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing
3. Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing
4. Abundance Estimation Using Discontinuity Preserving and Sparsity-Induced Priors
5. Superpixel-Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing
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1. AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising;IEEE Transactions on Geoscience and Remote Sensing;2024
2. Guided Filter of Random Patches Network and Relaxed-Collaborative-Representation-Based Hyperspectral Image Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
3. Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing;2023 10th International Conference on Recent Advances in Air and Space Technologies (RAST);2023-06-07
4. Locality Constraint Joint-Sparse and Weighted Low-Rank Based Hyperspectral Image Classification;2023 10th International Conference on Recent Advances in Air and Space Technologies (RAST);2023-06-07
5. Spatial–Spectral Multiscale Sparse Unmixing for Hyperspectral Images;IEEE Geoscience and Remote Sensing Letters;2023
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