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Joint local abundance sparse unmixing for hyperspectral images
Mia Rizkinia
, Masahiro Okuda
Department of Electrical Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
36
Citations (Scopus)
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Dive into the research topics of 'Joint local abundance sparse unmixing for hyperspectral images'. Together they form a unique fingerprint.
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Keyphrases
Hyperspectral
100%
Sparse Unmixing
100%
Local Abundance
100%
Low-rank
75%
Endmember
75%
Local Region
50%
Sparsity
50%
Optimization Problem
25%
Unique Properties
25%
Spatial Data
25%
Regularizer
25%
Pure pixel
25%
Optimal Fraction
25%
Performance Estimation
25%
Abundance Matrix
25%
Competitive Results
25%
Spatial Correlation
25%
Unmixing Problem
25%
Mixed pixel
25%
High Spatial
25%
L2,1-norm
25%
Total Variation
25%
Conventional Algorithm
25%
Nuclear Norm
25%
Computer Science
unmixing
100%
Hyperspectral Image
100%
Sparsity
66%
Optimization Problem
33%
Nuclear Norm
33%
Estimation Performance
33%
Unique Property
33%
Spatial Correlation
33%
Spatial Information
33%
Conventional Algorithm
33%
Hyperspectral Data
33%
Total Variation
33%
Mathematics
Matrix (Mathematics)
100%
Spatial Correlation
100%
Total Variation
100%
Earth and Planetary Sciences
Hyperspectral Image
100%