Novel Data-Driven Spatial-Spectral Correlated Scheme for Dimensionality Reduction of Hyperspectral Images
Author:
Affiliation:
1. Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong
2. Department of Electrical and Electronic Engineering, Universiti Tunku Abdul Rahman (UTAR), Kuala Lumpur, Malaysia
Funder
Research Grants Council of Hong Kong
Asian Office of Aerospace Research and Development
National Natural Science Foundation of China
HKU Seed Fund
University of Hong Kong
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09772246.pdf?arnumber=9772246
Reference63 articles.
1. Joint sparse model-based discriminative K-SVD for hyperspectral image classification
2. Multifeature-Based Discriminative Label Consistent K-SVD for Hyperspectral Image Classification
3. Hyperspectral Image Dimension Reduction Using Weight Modified Tensor-Patch-Based Methods
4. Spatial-Spectral Tensor Graph Convolutional Network for Hyperspectral Image Classification
5. Dimensionality reduction for hyperspectral data based on class-aware tensor neighborhood graph and patch alignment;gao;IEEE Trans Neural Netw Learn Syst,2015
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