Jagmal Singh(1)
Anca Popescu(2), Matteo Soccorsi(1), and Mihai Datcu(1)
(1) German Aerospace Center, Oberpfaffenhofen, Germany(2) Politehnica University Bucharest, Romania
Mining Very High Resolution InSAR Data based on Complex-GMRF Cues
and Relevance Feedback
Source:http://www.defenseindustrydaily.com/the-gps-constellation-now-and-future-01069/
• Overview
Fourier Transform basedSpectral Descriptors
SLC SAR data / InSAR Data
Complex-Gauss MarkovRandom Fields
RF-SVM based ClassifierEvaluationMeasures
Data Model Generation
Parametric Approach Non-parametric Approach
• Overview
Fourier Transform basedSpectral Descriptors
SLC SAR data / InSAR Data
Complex-Gauss MarkovRandom Fields
RF-SVM based ClassifierEvaluationMeasures
Data Model Generation
Parametric Approach Non-parametric Approach
• Complex-GMRF• Considering the SAR signal as the complex envelope of a
zero-mean band-limited Gaussian process, the GMRF model for complex-valued pixels (x+iy) can be written as:
Parametric Approach
Singh, J., Soccorsi, M., and M. Datcu, Parametric versus non-parametric complex image analysis, Proceedings of IGARSS 2009.
• Complex-GMRF
• Complex-valued patch
Parametric Approach
Based on the linear model proposed in Picinbono & Bouvet (1984)
• Complex-GMRF
Parametric Approach
Clique matrix : Datcu et. al. 2004
• Overview
Fourier Transform basedSpectral Descriptors
SLC SAR data / InSAR Data
Complex-Gauss MarkovRandom Fields
RF-SVM based ClassifierEvaluationMeasures
Data Model Generation
Parametric Approach Non-parametric Approach
• Fourier Transform based Spectral Descriptors
FFT
FFT
• Mean• Variance• Spectral Centroid in Range• Spectral Centroid in Azimuth• Spectral Flux in Range• Spectral Flux in Azimuth• Spectral Rolloff
Non-parametric Approach
motivated from timbral texture features used for music genre classificationT. Li, and M. Ogihara, “Towards Intelligent Music Information retrieval,” IEEE Transactions on Multimedia, pp. 564-574, June 2006.
• Fourier Transform based Spectral Descriptors1. Mean
2. Variance
3. Spectral Centroid In Range4. Spectral Centroid in Azimuth
5. Spectral Flux in Range6. Spectral Flux in Azimuth
7. Spectral Rolloff
Non-parametric Approach
• Overview
Fourier Transform based Spectral Descriptors
SLC SAR data / InSAR Data
Complex-Gauss Markov Random Fields
RF-SVM based ClassifierEvaluationMeasures
Data Model Generation
Parametric Approach Non-parametric Approach
• Data Model Generation
• TerraSAR-X High Resolution Spotlight Images
• Test sites:– Las-Vegas– Beijing– Bucharest
• Data Model Generation• Data-Set – Las-Vegas
• Data Model Generation• Data-Set – Beijing
• Data Model Generation• Data-Set – Bucharest
• Data Model Generation
• Data Model GenerationPatch
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• Data Model Generation
Why large size analyizing window !
• Data Model Generation
• Total number of image patches : 4500 for SLC4500 for InSAR
• Size of patch : 200 x 200 pixels
• InSAR data parameters:Las-Vegas : Ascending orbit, Right-look direction,
Effective baseline = 41.02 mBeijing : Ascending orbit, Right-look direction,
Effective baseline = 27.35 mBucharest : Ascending orbit, Right-look direction,
Effective baseline = 105.20 m
Pat
ch Q
uick
Lo
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Primitive FeatureExtraction Blocks
SLC/InSARImage
Data Base
SpectralFeatures
FeaturesData Base
GMRF
• Data Model Generation
Pat
ch Q
uick
Lo
oks
Primitive FeatureExtraction Blocks
SpectralFeatures
FeaturesData Base
GUI
Results / Examples
Image PatchIndexing
ClassData Base
ClassData Base
ClassData Base
ClassData Base
RelevanceFeedback
IndexValidation
SVM
• Support Vector Machine based Classifier
GMRF
SLC/InSARImage
Data Base
Pat
ch Q
uick
Lo
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Primitive FeatureExtraction Blocks
Image PatchesFeatures
Data BaseGUI
Results
ClassData Base
ClassData Base
ClassData Base
ClassData Base
SVM
Relavent and Negative Examples
RetrievedImage Patchs
EvaluationMeasures
• Support Vector Machine based Classifier
SpectralFeaturesGMRF
SLC/InSARImage
Data Base
Feature Space
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+
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Relevance Feedback Mechanism
Pat
ch Q
uick
Lo
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Primitive FeatureExtraction Blocks
SpectralFeatures
FeaturesData Base
GUI
Results / Examples
Image PatchIndexing
ClassData Base
ClassData Base
ClassData Base
Class-1Data Base
RelevanceFeedback
IndexValidation
SVM
GMRF
SLC/InSARImage
Data Base
• Object Categories
• Object Categories – Complex-GMRF
Category-1
Category-2
Category-3
Category-4
Category-5
Category-6
Category-7
• Object Categories – Complex-GMRF
• Performance Evaluation
Precision = True Positive / True Positive + False PositiveRecall = True Positive / True Positive + False Negative
• Performance Evaluation
F-Score = 2 × Precision × Recall / (Precision + Recall)
Tall Blocks
Stadium Bridge, Vegetation
House of
Parliament
(largest building
in Romania)
• Object Categories – Spectral Descriptors
Precision – Recall Fourier Spectrum based features on SLC data
Precision – Recall Fourier Spectrum based features on InSAR data
Class
Percen
t
Class
Percen
t
Accuracy (TP+TN)/(TP+TN+FP+FN) for SLC and InSAR
Red: SLCBlack: INSAR
Class
Percen
t
• Conclusions
• Interferometric observations can provide added value in Mining High-Resolution SAR data in the case of categories and objects containing man-made coherent targets.
• Need is to explore more diverse categories and build a more reliable data-base.
Please also refer to the poster :Structure and Object Recognition using Very High Resolution InSAR Observations.by : Anca Popescu, Mihai Datcu
• Thank you for your attention...Questions ?
• Acknowledgements:Thanks to colleagues from SAR Signal Processing Department at DLR, Oberpfaffenhofen for providing support in preparing the InSAR data.
