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Review of Spectral Unmixing for Hyperspectral Imagery. Lidan Miao Sept. 29, 2005. Motivation. The wide existence of mixed signals In hyperspectral image, the measurement of a single pixel is usually a contribution from several materials ( endmembers ). What is spectral unmixing? - PowerPoint PPT Presentation
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Review of Spectral Unmixingfor Hyperspectral Imagery
Lidan Miao
Sept. 29, 2005
Motivation
• The wide existence of mixed signals– In hyperspectral image, the measurement of a single pixel is
usually a contribution from several materials (endmembers).
• What is spectral unmixing?– A process by which mixed pixel spectra are decomposed into
endmember signatures and their fractional abundances.
• Applications – Subpixel detection. – Classification– Material quantification
Data Mixing Model
• Linear mixing– Mixing scale is macroscopic and there is negligible interaction
among distinct endmembers.
• Nonlinear mixing– Mixing scale is microscopic. The incident radiations scattered
through multiple bounces involves several endmembers.
Linear model Nonlinear model
Linear Mixing Model
• Measurement model
– Observation vector – Material signature matrix– Abundance fractions
• Nonnegative and sum-to-one constraints
p p p x Ms n
1 2[ ]l c c M m m m
1 1 2[ ]Tc cs s s s
1 1 2[ ]Tl lx x x x
1
0, 1c
j jj
s s
Unmixing Algorithms
• Supervised – Obtain M from laboratory data or training samples.– With known M, unmixing is a least square problem.– Not reliable due to spatial and temporal variation in illumination
and atmospheric conditions.
• Unsupervised – Linear spectral mixture analysis (LSMA)
• Two steps: estimate M from given data + inversing.
– Independent component analysis (ICA)• How to interpret the system model?
– Lagrange constrained neural network (LCNN)• Pixel-based algorithm.
LSMA
• Assumptions– There exist at least one pure pixel for each class.– The material signature matrix is the same for all image pixels in
the scene.– The number of endmembers can be determined.
• Two-step process– Endmember detection
• Convex geometry-based approach (MVT, PPI, N-FINDR, VCA)• LSE-based approach (UFCLS, USCLS, UNCLS)
– Abundance estimation• Least square method (LSE, SCLS, NCLS, FCLS)• Orthogonal subspace projection (OPS)• Quadratic programming (QP)• Target constrained method (CEM)
Convex Geometry
• Convex hull– The set of all convex combinations of point in C.
• Simplex– Convex hull of k+1 affinely independent points.
• Strong parallelism between LSMA and CG.– Endmember detection is
equivalent to identifying vertices.
1 1 2 2 1conv | , 0, 1, , , 1k k i i kC C i k x x x x
Convex hull in R2 Simplex in R2
1 0 0, , are linearly independent.k x x x x
LSE-based Approach
• Minimize the LSE between the linear mixture model and estimated measurements.
• Select the brightest pixel as the first endmember,after each iteration, select the most distinct pixel as new endmember.
• Use currently selected endmembers to unmix and calculate LSE.
ˆ( )LSE x x x
ICA
• System model
• Application– Blind source separation (BSS) and deconvolution.
• Assumption– Mutually independent sources.
• Permutation and scaling problem:– This compromises its application in spectral unmixing
1 11 1 1
1
l N l c c N
T Tc
T Tl l lc c
m m
m m
X M S
x s
x s
WM PC
ICA in Spectral Unmixing
• Two interpretations of system model
1 11 1 1
1
l N l c c N
T Tc
T Tl l lc c
m m
m m
X M S
x s
x s
1 11 1 1
1
N l N c c l
T Tc
T TN N Nc c
s s
s s
X S M
x m
x m
–Vector xi is the stack notation of image of band i.
–Column of M is endmember signature.
–Vector si is the abundance of endmember i at all pixel positions.
–Sources are not mutually independent.
–Vector xi is the spectrum of pixel i.
–Vector mi is endmember signature.
–Row vector si is the abundance of pixel i.
–Sources are mutually independent.
LCNN
• System model– Pixel-by-pixel processing
• Two principles– Maximum entropy
• Given incomplete information, maximum entropy is the least bias estimate• Closed system, no energy exchange
– Minimum Helmholtz free energy • The minimum free energy is achieved at thermal equilibrium state.• Open system, nonzero energy exchange
p p p p x M s n1
0, 1, 0c
j j ijj
s s m
Minimum Energy-based LCNN
• Objective function
• Nonlinear programming formulation
• The problem in objective function cannot be solved by learning algorithm.
0 0 0,
1 1
min ( , ) ln( ) ( ) 1c c
TB j j B j
j j
H K T s s K T s
W sW s Wx s
1
1
minimize: ln( )
subject to:
1, 0, 1, ,
c
j jj
c
j jj
s s
s s j c
Wx s 0
Evaluation SystemSelection of spectral
signatures
Generation of simulated scene
Unmixing algorithm
Ground truth
Abundancemap
Endmembersignatures
Estimated abundance
Extractedendmembers
•1. Evaluation of abundance fraction
–Root mean square error (RMSE)
–Fractional abundance angle distance (FAAD)
•2. Evaluation of endmember signature
–Spectral angle distance (SAD)
–Spectral information divergence (SID)
1 2
Simulated Data
0.5 1 1.5 2 2.50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Spectral signature of four endmembers
( m)
Re
flect
an
ce (
%)
BiotiteAmmonio-jarositeChloriteHalloysite
Unmixing Results (1)
Unmixing Result (2)
0 100 200 3000
0.5
1True end-members
0 100 200 3000
0.5
1Estimated end-membersTrue abundance
20 40 60
20
40
60
Estimated abundance
20 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1True end-members
0 100 200 3000
0.5
1Estimated end-membersTrue abundance
20 40 60
20
40
60
Estimated abundance
20 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
VCA USCLS
• There exist pure pixels in the scene (SNR=30dB)
Unmixing Result (3)
0 100 200 3000
0.5
1True end-members
0 100 200 3000
0.5
1Estimated end-membersTrue abundance
20 40 60
20
40
60
Estimated abundance
20 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1True end-members
0 100 200 3000
0.5
1Estimated end-membersTrue abundance
20 40 60
20
40
60
Estimated abundance
20 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
0 100 200 3000
0.5
1
0 100 200 3000
0.5
1
20 40 60
20
40
6020 40 60
20
40
60
VCA USCLS
• There are no pure pixels in the scene (SNR=30dB)
Unmixing Result (4)
10 20 30 40 50-0.01
0
0.01
0.02
0.03
0.04RMSE of abundance
SNR(dB)
RM
SE
PPINFINDRVCA
10 20 30 40 500
10
20
30
40
50
60
70Angle between abundance
SNR(dB)
AA
D (
de
gre
e)
PPINFINDRVCA
10 20 30 40 500
5
10
15
20Angle between endmembers
SNR(dB)
SA
D (
de
gre
e)
PPINFINDRVCA
10 20 30 40 50
0
0.05
0.1
0.15
0.2
0.25Spectral information divergence
SNR(dB)
SID
PPINFINDRVCA
Experiments on Real Data (1)
component 1 component 2 component 3
component 4 component 5 N-FINDR
N-FINDR
component 1 component 2 component 3
component 4 component 5 component 6
VCA
Experiments on Real Data (2)
component 1 component 2
component 3 component 4
FastICA
component 1 component 2 component 3
component 4 component 5
USCLS
Conclusion
• Convex geometry-based methods can successfully extract endmembers.
• ICA is not a robust algorithm for spectral unmixing.• More works on LCNN.
– Spatial information?– Redefine the objective function?