1. Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I-38123 Povo, Trento, Italy Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I-38123 Povo, Trento, Italy Lorenzo Bruzzone Francesca Bovolo A SEMANTIC-BASED MULTILEVEL APPROACH TO CHANGE DETECTION IN VERY HIGH GEOMETRICAL RESOLUTION MULTITEMPORAL IMAGES E-mail: lorenzo.bruzzone@ing.unitn.it Web page: http://rslab.disi.unitn.it

2. University of Trento, Italy Outline 2Lorenzo Bruzzone, Francesca Bovolo Introduction on change detection in VHR images General approach to change detection in VHR images Experimental results 1 Conclusion Illustration on the use of the approach for the solution of a specific change detection problem 2 3 4 5

3. University of Trento, Italy Main assumption: unsupervised change-detection techniques generally assume that multitemporal images are similar to each other except for the presence of changes occurred on the ground. Problems: This assumption is seldom satisfied in VHR images due to:  the complexity of the objects present in the scene (which may show different spectral behaviors at two different dates even if their semantic meaning does not change);  the differences in the acquisition conditions (e.g., sensor acquisition geometry, atmospheric and sunlight conditions, etc.). Introduction: Change Detection in VHR Images 3Lorenzo Bruzzone, Francesca Bovolo

4. University of Trento, Italy July 2006 October 2005 Quickbird images acquired on a portion of the city of Trento (Italy) 4Lorenzo Bruzzone, Francesca Bovolo Introduction: Change Detection in VHR Images

5. University of Trento, Italy Aim of the Work 5Lorenzo Bruzzone, Francesca Bovolo We propose a general top-down approach to the definition of the architecture of change detection methods for multitemporal VHR images. The proposed approach: explicitly models the presence of different radiometric changes on the basis of the properties of the considered images extracts the semantic meaning of changes; identifies changes of interest with strategies designed on the basis of the specific application; exploits the intrinsic multiscale properties of the objects and the high spatial correlation between pixels in a neighborhood.

6. University of Trento, Italy 6Lorenzo Bruzzone, Francesca Bovolo Proposed Approach: Architecture Design Multitemporal data set Identification of the tree of radiometric changes Direct extraction of changes of interest Refined detection of the radiometric change of interest Change detection map Differential extraction of changes of interest by cancellation Selection of the strategy for detecting changes of interest Auxiliary information Detection of all radiometric changes Detection of the changes of interest

7. University of Trento, Italy 7Lorenzo Bruzzone, Francesca Bovolo Changes due to acquisition conditions (  Acq ) Differences in atmospheric conditions (  Atm ) Differences in acquisition system (  Sys ) Changes occurred on the ground (  Grd ) Vegetation Phenology (  veg ) Anthropic activity (  Ant ) Natural disasters (  Dis ) Environmental conditions (  Env ) Radiometric Changes(  rad ) Sensor view angle Sensor acquisition mode Type of sensor Seasonal effects Identification of the Tree of Radiometric Changes

8. University of Trento, Italy 8Lorenzo Bruzzone, Francesca Bovolo Proposed Approach: Architecture Design Multitemporal data set Identification of the tree of radiometric changes Direct extraction of changes of interest Refined detection of the radiometric change of interest Change detection map Differential extraction of changes of interest by cancellation Selection of the strategy for detecting changes of interest Auxiliary information Detection of all radiometric changes Detection of the changes of interest Change Vector Analysis, Context-sensitive techniques, etc.

9. University of Trento, Italy 9Lorenzo Bruzzone, Francesca Bovolo Detection of Changes of Interest Refined detection of the radiometric change of interest Non-relevant change 1 Detection of radiometric changes Non-relevant change 2 Non-relevant change N - + X1X1 X2X2 Direct detection of changes of interest Differential detection by cancellation Detection of change of interest 1 Detection of change of interest K X1X1 X2X2 - - + + ++ Map of changes

10. University of Trento, Italy 10Lorenzo Bruzzone, Francesca Bovolo O1O1 O2O2 P1P1 P2P2 X1X1 X2X2 Meta-levels fusion Map of a specific Radiometric change Pixel radiometry Geometric or statistic primitives Classification map, object map,… Multilevel Architecture: Semantic of Changes Pixel Meta-level ( px ) Primitive Meta-level ( p ) Object Meta-level ( o ) j=1,…,Jpx j=1,…,Jp j=1,…,Jo O P 

11. University of Trento, Italy October 2004July 2006Reference Map Data Set Description Study area: South part of Trento (Italy). Multitemporal data set: portion (380×430 pixels) of two images acquired by the Quickbird satellite in October 2004 and July 2006. Causes of Change: changes on the ground, seasonal changes, registration noise.

12. University of Trento, Italy 12Lorenzo Bruzzone, Francesca Bovolo Proposed Approach: Architecture Design Multitemporal data set Identification of the tree of radiometric changes Direct extraction of changes of interest Refined detection of the radiometric change of interest Change detection map Differential extraction of changes of interest by cancellation Selection of the strategy for detecting changes of interest Auxiliary information Detection of all radiometric changes Detection of the changes of interest Change Vector Analysis, Context-sensitive techniques, etc.

13. University of Trento, Italy Identification of the Tree of Radiometric Changes 13Lorenzo Bruzzone, Francesca Bovolo  Rad  sh  rn  Sys  Grd  Veg  Ant  at  gl bb Grassland New buildings Shadow changes Apple trees Registration noise

14. University of Trento, Italy Changes Tree and Detection Strategy 14Lorenzo Bruzzone, Francesca Bovolo  Rad  sh  rn  Sys  Grd Shadow changes Registration noise Identification of the tree of radiometric changes Refined detection of  Grd Detection of  sh Detection of radiometric Changes (CVA) Detection of  rn - + X1X1 X2X2 - + Differential detection by cancellation Map of changes

15. University of Trento, Italy Multilevel Representation of Radiometric Changes 15Lorenzo Bruzzone, Francesca Bovolo X1X1 X2X2 Pixel Meta-level ( px ) Primitive Meta-level ( p ) Magnitude of multispectral change vectors Shadow change index Parcel map Registration noise map Image radiometry Shadow Index Segmentation map S. Marchesi, F. Bovolo, L. Bruzzone, “A Context-Sensitive Technique Robust to Registration Noise for Change Detection in VHR Multispectral Images”, IEEE Transactions on Image Processing, Vol. 19, pp. 1877-1889, 2010. F. Bovolo, “A Multilevel Parcel-Based Approach to Change Detection in Very High Resolution Multitemporal Images,” IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 1, pp. 33-37, January 2009. L. Bruzzone and D. Fernández-Prieto, "Automatic Analysis of the Difference Image for Unsupervised Change detection," IEEE Trans. Geosci. Rem. Sens., vol. 38, pp. 1170-1182, 2000. V. J. D. Tsai, "A comparative study on shadow compensation of color aerial images in invariant color models," IEEE Trans. Geosci. Remote Sens., vol. 44, pp. 1661-1671, 2006.

16. University of Trento, Italy 16Lorenzo Bruzzone, Francesca Bovolo Proposed Approach: Block Scheme X1X1 X2X2 Shadow detection Parcel detection Multiscale analysis for  rn detection CVA Comparison  sh detection  rad detection  ={  nc,  Grd } Change-detection map Magnitude of multispectral change vectors Shadow change index Shadow index - - +

17. University of Trento, Italy 17Marzo 2011 Silvia Demetri Technique False Alarms Missed Alarms Total Errors Overall accuracy (%) CVA pixel-based 500599241492990.86 CVA parcel-based 3537102611379891.56 Proposed method 14708480995093.91 Experimental Results 95 90 85 80 Overall change detection accuracy (%) 90.86 91.56 93.91 CVA Pixel-based CVA parcel-based Proposed method

18. University of Trento, Italy 18Marzo 2011 Silvia Demetri Reference Map Change Detection map CVA parcel based Change detection map Proposed approach October 2005July 2006 Experimental Results

19. University of Trento, Italy We presented a general top-down approach to the definition of the architecture of change detection methods for multitemporal VHR images. The main concepts exploited for the definition of the change detection architecture are:  Modeling the types of radiometric changes expected between images;  Extracting the semantic meaning from radiometric changes. The approach proposed includes:  Direct detection of changes of interest or differential cancellation of uninteresting radiometric changes;  Multilevel and context-sensitive techniques;  Iterative strategy. The approach has been successfully applied to the definition of aneffective architecture for change detection between Quickbird images in different application scenarios. Conclusion 19Lorenzo Bruzzone, Francesca Bovolo