Incremental Dictionary Learning-Driven Tensor Low-Rank and Sparse Representation for Hyperspectral Image Classification
Author:
Affiliation:
1. School of Earth Sciences and Engineering, Hohai University, Nanjing, China
2. Image and Signal Processing (ISP) Group, University of València, València, Spain
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09956814.pdf?arnumber=9956814
Reference67 articles.
1. A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification
2. Discriminant Analysis of Hyperspectral Imagery Using Fast Kernel Sparse and Low-Rank Graph
3. Low-Rank and Sparse Representation for Hyperspectral Image Processing: A Review
4. Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification
5. Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification
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4. A Deep Sparse Representation with Random Dictionary for Hyperspectral Image Classification;International Journal of Wavelets, Multiresolution and Information Processing;2023-03-06
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