Multiscale quadtree model fusion with super-resolution for blocky artefact removal

Digital elevation model (DEM) integration by optimally fusing multiple measurements having different characteristics can play an important role in estimation and prediction of environmental changes and natural disasters. Multiscale or multiresolution modelling used for this purpose has attracted attention in terms of its rich modelling capability as well as computational efficiency. In particular, a multiscale Kalman smoother (MKS) with tree structure graph can provide a powerful, efficient algorithm based on the Markov property. However, due to the stair-like correlation from the tree structure, unrealistic artefacts can be generated in the fusion result. This is especially prominent on geographic surfaces or ocean surfaces that have naturally smooth characteristics. In this article, a super-resolution (SR) algorithm is applied to remove blocky artefacts. Using the algorithm, new measurements are generated in the area where blocky artefacts can arise. The measurement is then fused with vector-valued MKS. The results show that this novel image fusion scheme provides a smaller root mean square error (RMSE) as well as better visual inspection.

[1]  Paul W. Fieguth,et al.  Multiresolution optimal interpolation and statistical analysis of TOPEX/POSEIDON satellite altimetry , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Hong Chang,et al.  Super-resolution through neighbor embedding , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[3]  A. Willsky,et al.  Multiscale Gaussian Graphical Models and Algorithms for Large-Scale Inference , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[4]  P. J. J. Desmet,et al.  Comparison of Routing Algorithms for Digital Elevation Models and Their Implications for Predicting Ephemeral Gullies , 1996, Int. J. Geogr. Inf. Sci..

[5]  K. Clint Slatton,et al.  Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights , 2001, IEEE Trans. Geosci. Remote. Sens..

[6]  Takeo Kanade,et al.  Hallucinating faces , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[7]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[8]  G. Potamianos,et al.  Stochastic Simulation Techniques for Partition Function Approximation of Gibbs Random Field Images , 1991 .

[9]  K. Clint Slatton,et al.  Improved observational updating for efficient fusion of incomplete image data , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[10]  Patrick Pérez,et al.  Discrete Markov image modeling and inference on the quadtree , 2000, IEEE Trans. Image Process..

[11]  Michael C. Nechyba,et al.  Interpretation of complex scenes using dynamic tree-structure Bayesian networks , 2007, Comput. Vis. Image Underst..

[12]  K. C. Chou,et al.  Multiscale systems, Kalman filters, and Riccati equations , 1994, IEEE Trans. Autom. Control..

[13]  Giovanni Alberti,et al.  The TOPSAR interferometric radar topographic mapping instrument , 1992, IEEE Trans. Geosci. Remote. Sens..

[14]  K. Clint Slatton,et al.  Multiscale fusion of INSAR data for improved topographic mapping , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[15]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.