scene class recognition using high resolution sar/insar spectral decomposition methods
DESCRIPTION
University POLITEHNICA Bucharest Facult y of Electronics, Telecommunication s and Information Technology. SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS. Anca Popescu, Inge Gavat University Politehnica Bucharest (UPB) Mihai Datcu - PowerPoint PPT PresentationTRANSCRIPT
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SCENE CLASS RECOGNITION USINGHIGH RESOLUTION SAR/INSAR SPECTRAL
DECOMPOSITION METHODS
Anca Popescu, Inge GavatUniversity Politehnica Bucharest (UPB)
Mihai DatcuGerman Aerospace Center (DLR)
IGARSS 2011
24-29 July 2011, Vancouver, Canada
University POLITEHNICA BucharestFaculty of Electronics, Telecommunications and Information Technology
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Motivation: High resolution scene category indexing, large number of structures visible in urban sites
Non – parametric feature extraction methods
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Summary
• Introduction
• Data descriptors
• Spectral features
• Spectral components
• Experimental data
• Classification results
• Conclusions
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Introduction
Study of value adding processing methods for SLC and InSAR data, for scene class recognition
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Direct Estimation from image spectra
Regularization based estimation +
Bayesian Model order selection
Consistency validation
Introduction
Study of value adding processing methods for SLC and InSAR data, for scene class recognition
Concept: Make use of the information contained in the phase of the SAR signal
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Data descriptors – spectral features
Direct estimation from spectra based on spectral differences
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Data modelThe 2-D signal model:
),()}(2exp{1
, nnenfnfjyK
kkkknn
Where kkk jba
kk ff ,
),( nne
complex amplitude of the kth sinusoid
unknown frequencies of the kth sinusoid
2-D noise
Data descriptors spectral components estimation
,k kk ff ,
Problem: Estimate the parameters of the sinusoidal signals:
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J. Li, P. Stoica: “Efficient Mixed-Spectrum Estimation with Applications to Target Feature Extraction”, IEEE Transactions on Signal Processing, 44, 1996, 281-295
2
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K
kkkk nfnfjnnyC
Model choice: minimize NLS criterion:
Peak of the 2-D periodogram
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2, |),(|maxarg)ˆ,ˆ( nkfj
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Algorithm preparations: if αk and fk are known, minimize cost function for the kth sinusoid is:
Height of the peak (complex)
Data descriptors spectral components estimation
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Y1 ampl 1 freq 1
…………….
Y2 ampl 1 freq 1
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Y3 ampl 1 freq 1
ampl 2 freq 2
ampl 3 freq 3
…………….
ampl 2 freq 2
STEP 1STEP 2STEP 3
…………...
................
)'(cos.9
),(ˆ,ˆ;ˆ,ˆ,ˆ,ˆ),(.8
),(ˆ,ˆ;ˆ,ˆ,ˆ,ˆ),(.7
),(ˆ,ˆ;ˆ,ˆ,ˆ,ˆ),(3.6
)'(cos.5
ˆ,ˆ),(.4
),(ˆ,ˆ.3
ˆ,ˆ),(2.2
),(ˆ,ˆ1.1
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tlysignificanchangetdoesnfunctionteconvergencpracticaluntilIterate
nnyfromobtainedareffffromobtainedisnny
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tlysignificanchangetdoesnfunctionteconvergencpracticaluntilIterate
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Data descriptors spectral components estimation
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AKAIKE Information Criterion – model order selection
Estimates the expected Kullback-Leibler information between the model generating the data and a candidate model
)))](ˆ|([log(max yxgEE xyg
kyL ))|ˆ(log(
kyLAIC 2))|ˆ(log(2
Model selection: = parameter to be estimated from empirical data y
Best Model: Minimum AIC value
y = generated from f(x), X is a random variableLog likelihood function for model selection:
Kullback-leibler Information(distance between models)
Maximized log-likelihood(parameter estimation)
Data descriptors spectral components estimation
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Goal: asses parameter’s capability to discriminate scene classes
Evaluation: Accuracy = (TP+TN) / (TP+TN+FP+FN)
Methodology for scene class indexing
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Test Site – Bucharest, Romania
TerraSAR-X High Resolution Spotlight: LAN 130
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SLCProduct type: HS SSC Master: HS SSC Slave: HS SSC
Acquisition date: 30.09.2008 Acquisition date: 11.10.2008 Acquisition date: 30.09.2008Ground range resolution: 0.8905 m Ground range resolution: 0.8905 m Ground range resolution: 0.8905 m
Azimuth resolution: 1.1000 m Azimuth resolution: 1.1000 m Azimuth resolution: 1.1000 mNr. of looks: 1 Nr. of looks: 1 Nr. of looks: 1
Interferometric pair
Frequency of classes in database: dominant classes: tall blocks, green areas, urban fabric, commercial and industrial sites
Experimental data – TSX LAN-130Test Site – Bucharest, Romania
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1650 SLC patches, 200 x 200 m
Water course/ water bodyStadion
Very tall buildingTall Block
Industrial Site
Test Site – Patch database formation
Interferometric data
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Spectral Centroid, Flux,and Rolloff (azimuth and range)
Mean and variance of most significant 3 cepstral coefficients
Results – Spectral and cepstral parameters
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Relax parameter αk (modulus representation), selection of six random components from the estimated stack
Results – Spectral components
Dense urban area, mostly tall blocks
Green area, small vegetation
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Reconstructed data from estimated spectral components and original SAR patch
Results – Spectral Components
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0 5 10 15 20 250.94
0.95
0.96
0.97
0.98
0.99
1
Class
TNR Spectral features SLC
Results – Scene Class RecognitionSLC/InSAR
True Negative Rate indicator
0 5 10 150.94
0.95
0.96
0.97
0.98
0.99
1
Class
TNR InSAR Spectral features
23 scene classes discoverable with SLC database
15 scene classes discoverable with InSAR database
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Results – Scene Class RecognitionSLC – Spectral Features and Spectral
ComponentsAccuracy indicator
0 5 10 15 20 250.88
0.9
0.92
0.94
0.96
0.98
1
Accuracy Spectral features SLC
Accuracy Spectral Components SLC
Class
Spectral components
Spectral features
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Influence of interferogram spectral features on classfication accuracy
Results – Scene Class RecognitionSLC / InSAR
0 5 10 15 20 250.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
Class
Accuracy InSAR Spectral Features
Accuracy SLC Spectral Features
Large roadGreen
urban area
Sparsely vegetated areas
Very tall building
Tall BlockInSAR
SLC
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• Evaluation of the capability of spectral parameters to discriminate scene classes for complex HR TerraSAR-X data
• Spectral estimation method for high resolution data characterization and reconstruction, based on RELAX algorithm.
• Evaluation of the capability of spectral components to discriminate scene classes for complex HR TerraSAR-X data
• Accuracy of recognition better than 80% for main class training
• Acknowledgements to DLR team for providing the interferometric data: Nico Adam, Christian Minet, Nestor Yague-Martinez , Helko Breit.
Conclusions