Review on graph learning for dimensionality reduction of hyperspectral image
Author:
Affiliation:
1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Publisher
Informa UK Limited
Subject
Computers in Earth Sciences,Geography, Planning and Development
Link
https://www.tandfonline.com/doi/pdf/10.1080/10095020.2020.1720529
Reference52 articles.
1. Object-based classification of hyperspectral data using Random Forest algorithm
2. Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction
3. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
4. Local Block Multilayer Sparse Extreme Learning Machine for Effective Feature Extraction and Classification of Hyperspectral Images
5. Developing water quality retrieval models with in situ hyperspectral data in Poyang Lake, China
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