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  • 1. A General Approach to the Spatial Simplication of Remote Sensing Images Based on Morphological Connected Filters Mauro Dalla Mura †, , Jón Atli Benediktsson , Lorenzo Bruzzone† † Department of Information Engineering and Computer Science University of Trento. Faculty of Electrical and Computer Engineering University of Iceland. IGARSS 2011 24-29 July
  • 2. Outline 1 Introduction 2 General Approach for Image Simplication Connected Operators Methodology 3 Conclusion and Future Developments IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 2 / 19
  • 3. IntroductionRemote Sensing VHR Images QuickBird 60cm, Panchromatic image, Bam (Iraq) Geoeye 50cm, Pansharpened images, Vancouver (Canada) c Google ROSIS-03 1.3m, Hyperspectral image, Pavia (Italy) TerraSAR-X 1.1m, Spotlight SAR, Dorsten (Germany) The information extraction in remote sensing images is becoming increasingly complex due to the progressively higher spatial resolution of the current sensors. How to extract the informative components dealing with the huge amount of details? IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 3 / 19
  • 4. IntroductionRemote Sensing VHR Images QuickBird 60cm, Panchromatic image, Bam (Iraq) Geoeye 50cm, Pansharpened images, Vancouver (Canada) c Google ROSIS-03 1.3m, Hyperspectral image, Pavia (Italy) TerraSAR-X 1.1m, Spotlight SAR, Dorsten (Germany) The information extraction in remote sensing images is becoming increasingly complex due to the progressively higher spatial resolution of the current sensors. How to extract the informative components dealing with the huge amount of details? IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 3 / 19
  • 5. IntroductionImage Simplication Spatial simplication of the image. Pre-processing operation for many remote sensing applications: Image segmentation; Supervised/unsupervised thematic classication; Land cover change analysis; Object recognition and extraction; Denoising SAR images; Analysis of multiangular images. Image simplication leads to: noise reduction; exploiting the contextual relations; modeling spatial relations; removing or attenuating undesired details. Simplication of the image performed by spatial ltering, a 2-step procedure composed of: 1 selection of the lters parameters; 2 application of the operator on the image. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 4 / 19
  • 6. IntroductionImage Simplication (examples) VHR optical image - dierent simplications Which details should be removed? Which operator should be applied? And which lter parameters should be selected? It depends on the application and on the type of image. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 5 / 19
  • 7. IntroductionImage Simplication (examples) VHR optical image - dierent simplications Which details should be removed? Which operator should be applied? And which lter parameters should be selected? It depends on the application and on the type of image. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 5 / 19
  • 8. IntroductionImage Simplication (examples) VHR optical image - dierent simplications Which details should be removed? Which operator should be applied? And which lter parameters should be selected? It depends on the application and on the type of image. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 5 / 19
  • 9. IntroductionMotivation Issues related to image simplication The selection of the parameters of the lters is application dependent. Proper operators should be used. Manual operation. Aims of the work Dene a novel general approach to image simplication based on morphological connected operators; suitable for the processing of dierent types of images and dierent applications; suitable to be performed in an automated way. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 6 / 19
  • 10. General Approach for Image Simplication Connected OperatorsConnected Operators - Operators by Reconstruction Connected operators are morphological lters that process an image by only merging its at zones. Either completely remove or entirely preserve a region in the image. They do not distort the geometrical characteristics (e.g., shape, edges) of the structures in the image. Operators by Reconstruction Closing Closing by rec. Original image Opening by rec. Opening φB (f ) φB (f ) = Rf [δB (f )] R ε f B δ γR (f ) = Rf [εB (f )] γB (f ) IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 7 / 19
  • 11. General Approach for Image Simplication Connected OperatorsConnected Operators - Operators by Reconstruction Connected operators are morphological lters that process an image by only merging its at zones. Either completely remove or entirely preserve a region in the image. They do not distort the geometrical characteristics (e.g., shape, edges) of the structures in the image. Operators by Reconstruction Closing Closing by rec. Original image Opening by rec. Opening φB (f ) φB (f ) = Rf [δB (f )] R ε f B δ γR (f ) = Rf [εB (f )] γB (f ) Connected operators are suitable for the analysis of VHR images. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 7 / 19
  • 12. General Approach for Image Simplication Connected OperatorsConnected Operators - Attribute Filters Attribute lters are connected operators dened in the mathematical morphology framework and recently used for the analysis of remote sensing1 . Based on a measure (attribute) computed on the regions of an image. Filtering performed by removing the regions that do not fulll a condition (T ) which compares an attribute attr against a reference value λ (e.g., T = attr ≥ λ). Main operators: attribute thinning, γT ; attribute thickening, φT . Example: Dierent attributes Original image Area Standard deviation Moment of inertia Solidity 1 M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, Morphological attribute proles for the analysis of very high resolution images, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 10, pp. IGARSS 20113762, Oct. 2010. 3747  (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 8 / 19
  • 13. General Approach for Image Simplication MethodologyArchitecture of the Proposed General Approach X Parameters Selection Bank of Scenario 1 Y Connected Filters e. g., attribute thinning, attribute thickening, ... Application Scenario 2 Knowledge Scene Performance Knowledge Scenario 3 Assessment The proposed approach is composed of two modules that perform the operations of: IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 9 / 19
  • 14. General Approach for Image Simplication MethodologyArchitecture of the Proposed General Approach X Parameters Selection Bank of Scenario 1 Y Connected Filters e. g., attribute thinning, attribute thickening, ... Application Scenario 2 Knowledge Scene Performance Knowledge Scenario 3 Assessment The proposed approach is composed of two modules that perform the operations of: 1 selection of the parameters and operators; IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 9 / 19
  • 15. General Approach for Image Simplication MethodologyArchitecture of the Proposed General Approach X Parameters Selection Bank of Scenario 1 Y Connected Filters e. g., attribute thinning, attribute thickening, ... Application Scenario 2 Knowledge Scene Performance Knowledge Scenario 3 Assessment The proposed approach is composed of two modules that perform the operations of: 1 selection of the parameters and operators; 2 ltering. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 9 / 19
  • 16. General Approach for Image Simplication MethodologyFilter Selection and Operative Scenarios Operative Scenarios Information available as prior knowledge: X Parameters Selection 1 Scenario 1 % Scene Knowledge Scenario 1 Bank of Y % Connected Filters Application Knowledge e. g., attribute thinning, 2 Scenario 2 Application attribute thickening, ... % Scene Knowledge Knowledge Scenario 2 " Application Knowledge 3 Scenario 3 Scene Scenario 3 Performance " Knowledge Assessment Scene Knowledge " Application Knowledge IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 10 / 19
  • 17. General Approach for Image Simplication MethodologyFilter Selection and Operative Scenarios Operative Scenarios Information available as prior knowledge: X Parameters Selection 1 Scenario 1 % Scene Knowledge Scenario 1 Bank of Y % Connected Filters Application Knowledge e. g., attribute thinning, 2 Scenario 2 Application attribute thickening, ... % Scene Knowledge Knowledge Scenario 2 " Application Knowledge 3 Scenario 3 Scene Scenario 3 Performance " Knowledge Assessment Scene Knowledge " Application Knowledge IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 10 / 19
  • 18. General Approach for Image Simplication MethodologyFilter Selection and Operative Scenarios Operative Scenarios Information available as prior knowledge: X Parameters Selection 1 Scenario 1 % Scene Knowledge Scenario 1 Bank of Y % Connected Filters Application Knowledge e. g., attribute thinning, 2 Scenario 2 Application attribute thickening, ... % Scene Knowledge Knowledge Scenario 2 " Application Knowledge 3 Scenario 3 Scene Scenario 3 Performance " Knowledge Assessment Scene Knowledge " Application Knowledge IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 10 / 19
  • 19. General Approach for Image Simplication MethodologyData set Quickbird panchromatic image of 995×995 pixels, 0.6 m resolution. Acquired over a residential urban area of Bam, Iran. Panchromatic image IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 11 / 19
  • 20. General Approach for Image Simplication MethodologyScenario 1 - Application Knowledge %, Scene Knowledge % Aim: Generic reduction of the complexity of the image by reducing non informative components. Filtering aiming at reducing: 1 Noisy components ⇒ Removing small regions with values signicantly dierent from their surroundings; 2 Inter-object variability ⇒ Flattening small values dierences in homogeneous regions. Suitable to cope with most of the applications. Eases the interpretation of the scene. Exploits the contextual relations of the pixels. Fully automatic suitable for batch processing. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 12 / 19
  • 21. General Approach for Image Simplication MethodologyScenario 1 - Example Generic simplication performed by: γ T φT with area attribute (remove small bright and dark regions); γ T with T based on relations between the regions1 (merge nested regions). VHR image Building rooftop (80×60 pixels). 2789 at regions. Simplied image. 1059 regions. 1 V. Caselles and P. Monasse, Geometric Description of Images as Topographic Maps. Springer, 2010. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 13 / 19
  • 22. General Approach for Image Simplication MethodologyScenario 2 - Application Knowledge ", Scene Knowledge % Filters parameters selected according to the application. The translation of the characteristics of the objects of interest from the concept to the lter parameters. Example: Application: building extraction. Aim of the simplication: enhance rectangular regions Concept: keep rectangular regions. Filtering: attribute lter with criterion: {rectangularity > 0.5}); Automation Modeling the range of values of the features that drive the lters with fuzzy possibilistic functions. Defuzzify in order to get the values for the lters parameters. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 14 / 19
  • 23. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Buildings (Particulars) Panchromatic image (f ) Filter by rec. B f − γR (f ) (B : disk radius 7 pixels) Attribute lter γ T (f ) with T = (R > 0.3) ∧ (I < 0.5) ∧ (50 < A < 5000) R: rectangularity; I: moment of inertia; A: area IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
  • 24. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Buildings (Particulars) Panchromatic image (f ) Filter by rec. B f − γR (f ) (B : disk radius 11 pixels) Attribute lter γ T (f ) with T = (R > 0.3) ∧ (I < 0.5) ∧ (50 < A < 5000) R: rectangularity; I: moment of inertia; A: area IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
  • 25. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Buildings (Particulars) Panchromatic image (f ) Filter by rec. B f − γR (f ) (B : disk radius 15 pixels) Attribute lter γ T (f ) with T = (R > 0.3) ∧ (I < 0.5) ∧ (50 < A < 5000) R: rectangularity; I: moment of inertia; A: area IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
  • 26. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Buildings (Particulars) Panchromatic image (f ) Filter by rec. B f − γR (f ) (B : disk radius 19 pixels) Attribute lter γ T (f ) with T = (R > 0.3) ∧ (I < 0.5) ∧ (50 < A < 5000) R: rectangularity; I: moment of inertia; A: area IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 15 / 19
  • 27. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Dark Elongated Structures (Particulars) Panchromatic image (f ) Filter by rec. C[φB (f ) − f ] (B : R disk radius 3 pixels) Attribute lter φT (f ) with T = (H > 10000) ∧ (I > 1.0) H: height; I: moment of inertia IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
  • 28. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Dark Elongated Structures (Particulars) Panchromatic image (f ) Filter by rec. C[φB (f ) − f ] (B : R disk radius 7 pixels) Attribute lter φT (f ) with T = (H > 10000) ∧ (I > 1.0) H: height; I: moment of inertia IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
  • 29. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Dark Elongated Structures (Particulars) Panchromatic image (f ) Filter by rec. C[φB (f ) − f ] (B : R disk radius 11 pixels) Attribute lter φT (f ) with T = (H > 10000) ∧ (I > 1.0) H: height; I: moment of inertia IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
  • 30. General Approach for Image Simplication MethodologyScenario 2 - Enhancement of Dark Elongated Structures (Particulars) Panchromatic image (f ) Filter by rec. C[φB (f ) − f ] (B : R disk radius 15 pixels) Attribute lter φT (f ) with T = (H > 10000) ∧ (I > 1.0) H: height; I: moment of inertia IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 16 / 19
  • 31. General Approach for Image Simplication MethodologyScenario 3 - Application Knowledge ", Scene Knowledge " Ad hoc parameters selection. If the available information is a set of labeled samples (i.e., a training set), the reduction of the image complexity generated by the ltering aims at increasing the separability of the classes. Performance assessment. The quality of the simplication obtained can be evaluated on the known samples according to a given criterion. Automation Dene a cost function to minimize, representing the tness of the generated result with the input requirements; Dene a optimization procedure and a stopping condition. See on Thurstday: S. Peeters, P. R. Marpu, J. A. Benediktsson, M. Dalla Mura Classication using extended morphological attribute proles based on dierent feature extraction techniques. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 17 / 19
  • 32. Conclusion and Future DevelopmentsConclusion and Future Developments Conclusion Denition of a novel general approach for image simplication based on connected lters (in particular attribute lters). Suitable for processing many dierent image types; dierent applications involving image analysis. Contributions: identication of three scenarios modeling common dierent operative conditions; giving guidelines for the automation of the process; qualitative evaluation of the proposed approach on a real data set in dierent scenarios. Future Developments Extensively test the approach on dierent type of images and applications. Improve the automation of the process. IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 18 / 19
  • 33. Conclusion and Future DevelopmentsThanks for your attention! IGARSS 2011 (24-29 July) Mauro Dalla Mura dallamura@disi.unitn.it 19 / 19