Global-Local Balanced Low-Rank Approximation of Hyperspectral Images for Classification

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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Mathematics

100
Low-Rank Approximat... Mathematics
66
Detection Mathematics
66
Pixel Mathematics
33
Superiority Mathematics
33
Algorithm Mathematics
33
Variation Mathematics
33
Data Set Mathematics

Computer Science

100
Low-Rank Computer Science
100
Rank Approximation Computer Science
100
Hyperspectral Image Computer Science
100
Procedure Computer Science
66
Remote Sensing Imag... Computer Science
33
Experimental Result Computer Science
33
Optimization Proble... Computer Science
33
Iterative Algorithm Computer Science
33
Classification Accu... Computer Science
33
Spectral Domain Computer Science
33
Spatial Domain Computer Science
33
Benchmark Computer Science
33
Spatial Information Computer Science
33
Discriminability Computer Science

Keyphrases

100
Global-local Keyphrases
100
Low-rank Approximat... Keyphrases
100
Hyperspectral Keyphrases
66
Hyperspectral Remot... Keyphrases
33
Optimization Proble... Keyphrases
33
Iterative Algorithm Keyphrases
33
Classification Accu... Keyphrases
33
Spectral Domain Keyphrases
33
Spatial Domain Keyphrases
33
Benchmark Dataset Keyphrases
33
Spatial Data Keyphrases
33
Spectral Variation Keyphrases
33
Low-rank Keyphrases
33
State-of-the-art Te... Keyphrases
33
Discriminative Repr... Keyphrases
33
Global Balance Keyphrases

INIS

100
approximations INIS
100
images INIS
100
classification INIS
66
remote sensing INIS
33
algorithms INIS
33
accuracy INIS
33
information INIS
33
balances INIS
33
variations INIS
33
iterative methods INIS
33
optimization INIS
33
benchmarks INIS
33
datasets INIS
Research OutputsResearch Output authored by Global-Local Balanced Low-Rank Approximation of Hyperspectral Images for Classification is tagged with the concept