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Parametric adaptive signal detection for hyperspectral imaging
Hongbin Li
, James H. Michels
Department of Electrical and Computer Engineering
School of Engineering and Science
Research output
:
Contribution to journal
›
Article
›
peer-review
21
Scopus citations
Overview
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Earth and Planetary Sciences
Training
100%
Signal Detection
100%
Hyperspectral Imaging
100%
Model
42%
Estimate
28%
Pixel
28%
Target
28%
Covariance
28%
Size
28%
Convention
14%
Detection
14%
Image
14%
Parameter
14%
Order
14%
Need
14%
Requirement
14%
Approach
14%
Environment
14%
Selection
14%
Time Series
14%
Class
14%
Physics
Signal Detection
100%
Model
100%
Covariance
66%
Target
66%
Estimate
66%
Detection
33%
Independent Variables
33%
Images
33%
Environment
33%
Dimension
33%
Mathematics
Parametric
100%
Detection
100%
Pixel
66%
Covariance Matrix
66%
Parametric Model
66%
Selection
33%
Maximum Likelihood Estimator
33%
Class
33%
Sensor
33%
Order
33%
Engineering
Models
100%
Signal Detection
100%
Covariance Matrix
66%
Images
33%
Experimental Result
33%
Sensor
33%
Maximum Likelihood
33%
Estimator
33%
Large Size
33%
Environment
33%
Detection
33%
Requirement
33%
Computer Science
Signal Detection
100%
Hyperspectral Imaging
100%
Parametric Model
40%
Covariance Matrix
40%
Detection
20%
maximum-likelihood
20%
Identification
20%
Imaging Sensor
20%
Training Requirement
20%
Model
20%
Class
20%
Experimental Result
20%
Spectral Dimension
20%