slides7.dviEECE–4649
Northeastern University
Why Hyperspectral
South Bay of California; 101 curves down on the left.
June 2019 Chuck DiMarzio, Northeastern University 12286..slides7–2
Why Hyperspectral
Where are the Shrimp?
Hardware
– x, y on camera
∗ Slit, Grating and 2D Camera
· x, λ on camera
· y with time (pushbroom)
Applications
• Law Enforcement (Which crop is illegal?)
• Environmental (e.g. Deep Horizon)
rophores)
Forward Models
• Monte–Carlo
• Fluorescence
Some Common Absorbers
,Wavelength, nm
10 -4
10 -3
10 -2
10 -1
10 0
10 1
10 2
10 3
10 4
Some Common Fluorophores
, Wavelength, nm
488 514 633
Matrix Methods
• Forward Problem
Y = MX
• Typical Example
June 2019 Chuck DiMarzio, Northeastern University 12286..slides7–9
Variance, Covariance
• Covariance:
Y =
June 2019 Chuck DiMarzio, Northeastern University 12286..slides7–10
Variance, Covariance, Principal Components
• Arrange in Eigenvalue Order Decending
• Remember Y = MX for a pixel or Y = MX for an image
• It’s Possible from this to Guess
– M, the basis comonent spectra in columns
– X , the strength of each component in each pixel
June 2019 Chuck DiMarzio, Northeastern University 12286..slides7–11
Non–Negative Least Squares
June 2019 Chuck DiMarzio, Northeastern University 12286..slides7–12