Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Author:
Affiliation:
1. Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
2. INRIA, CNRS, Grenoble Institute of Technology (Grenoble INP), LJK, Université Grenoble Alpes, Grenoble, France
Funder
Agence Nationale de la Recherche (ANR) 3IA MIAI@Grenoble Alpes
European Research Council (ERC) Grant through the APHELAIA Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/36/10354519/10508406.pdf?arnumber=10508406
Reference221 articles.
1. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
2. Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms
3. Spectral Variability in�Hyperspectral Data�Unmixing: A Comprehensive Review
4. Hyde: The First Open-Source, Python-Based, Gpu-Accelerated Hyperspectral Denoising Package
5. Noise Reduction in Hyperspectral Imagery: Overview and Application
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1. Models and Algorithms of Hyperspectral Image Anomaly Detection Based on Low-Rank Technology;2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL);2024-04-19
2. Fast Semisupervised Unmixing Using Nonconvex Optimization;IEEE Transactions on Geoscience and Remote Sensing;2024
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