Spatial and Temporal Information Fusion Based on Laplace ...optimization/L1/optseminar... · ETM...
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Spatial and Temporal Information Spatial and Temporal Information Fusion Based on Laplace Pyramid Fusion Based on Laplace Pyramid
Peng, Zhimin
Advisor: Prof. Huang, Bo (CUHK)
Dr. Meng, Deyu (XJTU)
Outline:Outline:
� Problem
� Existing Method
� Framework
Results� Results
� Future work
Viewing the entire Earth's surface every 1 to 2 days (high temporal resolution)
Obtain image with low spatial resolution
(250m)
TERRA
MODIS
(250m)
Viewing the entire Earth's surface every 16 days, (low temporalresolution)
Obtain image with high spatial resolution
(30m)
LANDSAT
ETM
(30m)
Crop
High Spatial and High Temporal Crop
Growing Desertification UrbanizationHigh Temporal
resolution remote sensing data
Problem:Problem:
A B
A’ B’
Existing MethodsExisting Methods
� STARFM (Gao, Masek, et al. 06)
FrameworkFramework
Step 1:Step 1:
Laplace Pyramid Laplace Pyramid Laplace Pyramid Laplace Pyramid DecompositionDecomposition
Laplace Pyramid(LP)Laplace Pyramid(LP)
� Introduced by Burt and Adelson in 1983
� Function:
Each level represents a different level band of spatial frequenciesband of spatial frequencies
� Basic idea:
Step 1: Build a Gaussian Pyramid
Step 2: Take the difference between one
Gaussian Pyramid level and the next
Gaussian PyramidGaussian Pyramid
Laplace PyramidLaplace Pyramid
Step 2: Step 2:
Match FunctionMatch FunctionMatch FunctionMatch Function
Match FunctionMatch Function
Level i
iM'
iM
Image Analogies , Aaron Hertzmann, etc, 2002
Level i
iE'
iE
),,( ''
iiii MEEionMatchFunctE =
Match FunctionMatch Function
� Scheme 1:
� — Block by Block Regression
� Scheme 2:� Scheme 2:
� — Markov Network
Block by Block RegressionBlock by Block Regression
� Assumption:
� Patches of the same location satisfy linear relationship
� i.e.
Combine the above two equations
Markov NetworkMarkov Network
iM
iE
'M
Dictionary Preparation:
'
iM
'
iE Candidates
Markov NetworkMarkov Network
Belief Propagation
Step 3: Step 3:
ReconstructionReconstructionReconstructionReconstruction
ReconstructionReconstruction
� Usual Reconstruction :
Simply added back the different frequency part
from coarser to finer
Sensitive to noiseSensitive to noise
� New Reconstruction:
Frame Theory (Minh N. Do 2003)
Robust
Usual ReconstructionUsual Reconstruction
� Decomposition
� Reconstruction
Noise Pyramid: y y e= +ɶ
New ReconstructionNew Reconstruction
2( ) ( )
TG H
GH GH
=
=
† 1( )T TS A A A A−
= =
Computational Expensive
TA A I=( ) ( )GH GH=
†ˆ ( )x A y G c Hd d= = − +
ComparisonComparison
Landscape change monitoringLandscape change monitoring
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Real ETM STARFM
Markov Network Regression
Scatter graphScatter graph
Our Method STARFM
Seasonal ChangeSeasonal Change
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Original Our Method STARFM
Future workFuture work
� Improve prediction accuracy
� Retain detail information
— Compressive Sensing
— Matrix Completion— Matrix Completion
� Explore information contained in other band
Thank you !Thank you !Thank you !Thank you !