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Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
Multitemporal region-based classification of high-resolution images by Markov random fields.pdf
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Multitemporal region-based classification of high-resolution images by Markov random fields.pdf

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  • 1. IGARSS-2011 Vancouver, Canada, July 24-29, 2011Multitemporal Region-Based Classification of High-Resolution Images by Markov Random Fields and Multiscale Segmentation Gabriele Moser Sebastiano B. Serpico University of Genoa Department of Biophysical and Electronic Engineering
  • 2. 2 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 3. 3 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 4. 4 Introduction Peking, SPOT-5, 2003 SPOT- • Multitemporal high-resolution (HR) optical image classification: – Important for environmental monitoring (e.g. in disaster prevention and management), urban area analysis, etc. – 0.5 ÷ 10 m resolution by current (e.g., IKONOS, QuickBird, WorldView-2, GeoEye-1, SPOT-5 HRG) and forthcoming (e.g., Pleiades) missions. Peking, SPOT-5, 2005 SPOT- – Need for modeling the temporal and spatial- geometrical information in multitemporal HR data. • A supervised multitemporal multiscale region- based HR image classifier is proposed, based on: – Multiscale segmentation; – Markov random field (MRF) models.University of Genoa Department of Biophysical and Electronic Engineering
  • 5. 5 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 6. 6 The Proposed Method• Motivation – Key role of spatial-contextual and multiscale information in HR image analysis. – Need for modeling the temporal context associated to multitemporal images. • Key-ideas – Extracting multiscale information at each acquisition time by generating segmentation maps at different scales. – Fusing spatial, temporal, and multiscale information by a novel region-based MRF model – The model extends two previous models for single-time multiscale and single-scale multitemporal classification, respectively. University of Genoa Department of Biophysical and Electronic Engineering
  • 7. 7 Architecture of the Proposed Method Segmentation map S10 at the finest scale HR Graph-basedimage X0 segmentationat time t0 Segmentation map S20 at intermediate scale ClassificationTraining ··· map for time t0map for Segmentation map SQ0 time t0 at the coarsest scale MRF-based multiscale and multitemporal fusion Segmentation map S11Training at the finest scalemap for time t1 Classification Segmentation map S21 map for time t1 at intermediate scale HR Graph-basedimage X1 segmentation ···at time t1 Segmentation map SQ1 at the coarsest scale University of Genoa Department of Biophysical and Electronic Engineering
  • 8. 8 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 9. 9 Multitemporal Markov Random Fields • MRF model for the spatio-temporal context – Representation of the statistical interactions between the pixel labels at each time tr (r = 0, 1) by using only local relationships in both the spatial and temporal domains: ( ) ( P ℓ ir { ℓ jr } j ≠i ,{ ℓ k ,1−r } = P ℓ ir { ℓ jr } j ∼i , { ℓ k ,1−r } k ≃ i ) Conditioning only to thelabels of the (spatially or Time tr Time t1 – r temporally) neighboring pixels, according to a j given neighborhood i i system (here 3 × 3) k Spatial context of the i-th pixel Temporal context of the i-th pixel in the image at each time tr in the image at the other time t1 – r University of Genoa Department of Biophysical and Electronic Engineering
  • 10. 10 Energy Function • MRF-based classification – Minimization of a (posterior) energy function U(·), thanks to the Hammersley-Clifford theorem. – When using MRFs for data fusion, U(·) is a linear combination of energy contributions, each related to an information source. • Proposed region-based multitemporal MRF model – Fusion of spatial, temporal, and multiscale information: Transition 1  Q U ( L0 , L1 S0 ,S1 ) = − ∑  ∑∑ α qr ln P (siqr | ℓ ir ) + probability from r =0  i q =1 each class at a time to each  class at the Pixelwise probability mass + βr ∑ δ (ℓ ir , ℓ jr ) + γ r ∑ P (ℓ ir | ℓ k ,1−r ) other time function (PMF) of the segment i∼j i ≃k labels in the segmentation map at each scale and each date, Potts model for the conditioned to each class spatial context University of Genoa Department of Biophysical and Electronic Engineering
  • 11. 11 Segmentation and PMF Estimation• Felzenszwalb & Huttenlocherm segmentation method [3] – Graph-based region-growing method depending on a scale parameter. – Segmentations at different scales by varying this parameter.• Class-conditional PMF estimation – Extension of a previous method proposed for a single-time region-based MRF. – Relative-frequency estimate, based on preliminary classification maps M0 and M1. – M0 and M1 generated here by a classical MRF classifier with k-NN estimates of the pixelwise class-conditional statistics. University of Genoa Department of Biophysical and Electronic Engineering
  • 12. 12 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 13. 13 Parameter Estimation and Energy Minimization• Transition probabilities: expectation-maximization (EM) – Converges (under mild assumptions) to ML estimates. – Uses the aformentioned k-NN pixelwise estimates.• Weight parameters (α, β, γ) in the MRF – Extension of a recent method based on the Ho-Kashyap algorithm.• Energy minimization: iterated conditional mode (ICM) – Initialized with the preliminary maps M0 and M1. – Converges to a local energy minimum. – Usually good tradeoff between accuracy and time. University of Genoa Department of Biophysical and Electronic Engineering
  • 14. 14 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 15. 15 Data Sets and Experimental Set-up • SPOT-5 HRG data set 2003 – Peking (China), 4 bands, 1500 × 1160 pixels, 10-m resolution, acquisitions in 2003 and 2005. • QuickBird data set – Phoenix (AZ), 4 bands, 300 × 300 pixels, 2.8-m resolution, acquisitions in 2003 and 2005. 2005 • Experimental comparisons – With a previous multitemporal non-region-based single-scale classifier based on MRFs [6]. 2005 – With a previous multitemporal non-contextual single-scalePeking: false color RGB Phoenix: true color RGB classifier based on EM [8]. University of Genoa Department of Biophysical and Electronic Engineering
  • 16. 16 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 17. 17 “Phoenix”: Classification Maps 2003 2005True color RGB EM method in [8] MRF method in [6] Proposed method urban shrub-brush herbaceous barren transitional University of Genoa Department of Biophysical and Electronic Engineering
  • 18. 18 “Peking”: Classification Maps False color RGB urban agricultural2003 2005 rangeland barren water Proposed method University of Genoa Department of Biophysical and Electronic Engineering
  • 19. 19 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – Data sets and experimental set-up – Classification maps – Classification accuracies and comparisons• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 20. 20 Classification Accuracies– Application with up to 5 scales.– Accurate classification maps with “Phoenix” (overall accuracy, OA ≈ 94 ÷ 98%; average accuracy, AA ≈ 92 ÷ 96%).– Quite accurate maps with “Peking” (OA ≈ 84%, AA ≈ 85%): strong spectral overlapping between the classes (especially “urban vs. barren land”). University of Genoa Department of Biophysical and Electronic Engineering
  • 21. 21 Experimental Comparisons– More accurate results by the method in [8] (MRF fusion of spatio-temporal context) than by the one in [6] (modeling only temporal correlation).– More accurate results by the proposed method than by both previous techniques. This suggests the effectivenes of the method in fusing spatial, temporal, and multiscale information for region-based multitemporal classification. University of Genoa Department of Biophysical and Electronic Engineering
  • 22. 22 Outline• Introduction – Multitemporal high-resolution image classification• The proposed method – Overall architecture – Multitemporal multiscale region-based Markov random field – Parameter estimation and energy minimization• Experimental results – With QuickBird images – With SPOT-5 HRG images• Conclusion University of Genoa Department of Biophysical and Electronic Engineering
  • 23. 23 Conclusion• A novel multitemporal classifier has been proposed for HR images, based on a region-based multiscale MRF. – Capability to fuse spatio-temporal context and multiscale information associated to a multitemporal HR data set. – Accurate results with different sensors (QuickBird, SPOT-5) and resolutions (2.8 m, 10 m). – Accuracy improvements, compared to previous multitemporal methods based on temporal or spatio-temporal models. – Confirmation of the relevance of multiscale information in HR image analysis.• Possible future generalizations – Integrating edge and/or texture information. – Approaching global energy minimization (e.g., graph-cuts). – Comparisons with other techniques for multitemporal VHR classification University of Genoa Department of Biophysical and Electronic Engineering
  • 24. 24 References1. S. Li, Markov random field modeling in image analysis, Springer, 20092. A. H. S. Solberg, T. Taxt, and A. K. Jain, “A Markov random field model for classification of multisource satellite imagery,” IEEE Trans. Geosci. Remote Sensing, vol. 34, no. 1, pp. 100–113, 19963. P. F. Felzenszwalb and D. Huttenlocherm, “Efficient graph-based image segmentation,” Int. J. Comp. Vis., vol. 59, pp. 167–181, 20044. F. Causa, G.Moser, and S. B. Serpico, “A novel MRF model for the detection of urban areas in optical high spatial resolution images,” Rivista Italiana di Telerilevamento, vol. 38, 20075. G. Moser and S. B. Serpico, “Classification of high-resolution images based on MRF fusion and multiscale segmentation,” in Proc. of IGARSS-2008, Boston, USA, 2008, vol. II, pp. 277–2806. F. Melgani and S. B. Serpico, “A Markov random field approach to spatio-temporal contextual image classification,” IEEE Trans. Geosci. Remote Sensing, vol. 41, no. 11, pp. 2478–2487, 20037. S. B. Serpico and G. Moser, “Weight parameter optimization by the Ho-Kashyap algorithm in MRF models for supervised image classification,” IEEE Trans. Geosci. Remote Sensing, vol. 44, pp. 3695–3705, 20068. L. Bruzzone and S. B. Serpico, “An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images,” IEEE Trans. Geosci. Remote Sensing, vol. 36, pp. 858–867, 19979. R. A. Redner and H. F. Walker, “Mixture densities, maximum likelihood, and the EM algorithm,” SIAM Review, vol. 26, no. 2, pp. 195– 239, 198410. F. Bovolo, “A multilevel parcel-based approach to change detection in very high resolution multitemporal images,” IEEE Geosci. Remote Sensing Lett., vol. 6, no. 1, pp. 33–37, 2009 University of Genoa Department of Biophysical and Electronic Engineering

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