CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: CURVELET APPROACH FOR SAR IMAGE DENOISING, STRUCTURE ENHANCEMENT, AND CHANGE DETECTION Andreas Schmitt, Birgit Wessel, Achim Roth

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 156 inherent noise is reduced and underlying structures are enhanced depending on their length, their orientation or their intensity. In the image enhancement context this approach is most suitable for fine-structured areas, e.g. city centers. The main problem lies in the determination of thresholds for suppression and em phasis of structures. The determination of the threshold and the number of coefficients respectively is still experiential and highly dependent on the image content. If the scenes are reconstructed by a fix number of coefficients, the complexity of the scene is restricted. As the image description by the curvelet coefficients is purely based on structures, by omitting coefficients originally smooth areas are often affected by artifacts. At the moment the quadratic weighting of the single curvelet coefficients seems to be the best solution for fully automatic processing chains. The change detection approach provides excellent results in ur ban areas. The great advantage over pixel based methods is the sensitivity towards changes in structures and the possibility to predefine the scale and the strength of changes to be mapped. Problems occur in natural surroundings like forested areas, where the status of the foliage has an important seasonal impact on the backscattering behavior. Not to mention the weather conditions, snow cover with different moistures can highly modify the ap pearance in a SAR image. In consequence of that the interpre tation of the detected changes is very challenging. Although the change images contain clear structures without any disturbances, it is nearly impossible to distinguish man-made from natural, e.g. seasonal, changes, without a priori knowledge about the land cover. As the present results proved that two single polarized SAR im ages can be used to indicate changes happened to the imaged area, but they do not provide the information needed to interpret these changes, our future research will try to include other data sources into the processing chain. To discriminate natural cover from man-made objects, a coherence layer, that exploits the phase information of the input images could be helpful. Polarimetrie layers could facilitate the interpretation by attaching information about the scattering types to the detected changes. Apart from remote sensing data it is quite conceivable to introduce a priori knowledge by overlaying the change layer with land cover classi fications from optical data sources as well as with cadastral data sets. References Ali, S. M., Javed, M. Y. and Khattak, N. S., 2007. Wavelet- Based Despeckling of Synthetic Aperture Radar Images Using Adaptive and Mean Filters. In: Proceedings of World Academy of Science, Engineering and Technology, Venice (Italy), Voi. 25, pp. 39-43. Balz, T„ 2004. SAR simulation based change detection with high-resolution SAR images in urban environments. In: IS- PRS Congress, Istanbul 2004, Proceedings of Commission VII, Voi. 35, pp. 472^77. Bouyahia, Z., Benyoussef, L. and Derrode, S., 2008. Change detection in synthetic aperture radar images with a sliding hid den Markov chain model. Journal of Applied Remote Sensing (JARS), SPIE. Candès, E. J. and Demanet, L., 2002b. Curvelets and Fourier integral operators. Compie Rendus de l’Academie des Sciences 336, pp. 395-398. Candès, E. J. and Donoho, D. L., 1999. Curve and Sur face Fitting. Innovations in Applied Mathematics, Vanderbilt University Press, Nashville (TN), Saint-Malo (France), chapter Curvelets - a surprisingly effective nonadaptive representation for objects with edges, pp. 105-120. Candes, E. J. and Donoho, D. L., 2002a. New Tight Frames of Curvelets and Optimal Representations of Objects with Smooth Singularities. Comm. Pure Appl. Math. 57, pp. 219-266. Candes, E. J. and Donoho, D. L., 2003a. Continuous Curvelet Transform II: Discretization and Frames. Appl. Comput. Har mon. Anal. 19, pp. 162-197. Candes, E. J. and Donoho, D. L., 2003b. Continuous Curvelet Transform I: Resolution of the Wavefront Set. Appl. Comput. Harmon. Anal. 19, pp. 198-222. Candes, E. J. and Guo, F., 2002. New Multiscale Trans forms, Minimum Total Variation Synthesis: Applications to Edge-Preserving Image Reconstruction. Signal Processing 82, pp. 1519-1543. Candes, E. J., Demanet, L., Donoho, D. L. and Ying, L., 2005. Fast Discrete Curvelet Transforms. Multiscale Model. Simul. 5, pp. 861-899. Demanet, L., 2007. curvelet.org. http://www.curvelet.org. (ac cessed on 26 March 2009). Derrode, S., Mercier, G. and Pieczynski, W., 2003. Unsuper vised Change Detection in SAR Images Using a Multicompo nent HMC model. In: P. Smits and L. Bruzzone (eds), Sec ond International Workshop on the Analysis of Multitemporal Remote Sensing Images, European Commission Joint Research Centre, Ispra (Italy), pp. 16-18. Donoho, D. L. and Duncan, M. R., 2000. Digital Curvelet Transform: Strategy, Implementation and Experiments. In: Aerosense 2000, Wavelet Applications VII, Orlando (FL), pp. 12-29. Gleich, D., Kseneman, M. and Datcu, M., 2008. Despeckling of TerraSAR-X data using second generation wavelets. In: ESA EUSC 2008: Image Information Mining, Frascati (Italy). Ma, L., Ma, J. and Shen, Y., 2007. Pixel Fusion Based Curvelets and Wavelets Denoise Algorithm. Engineering Letters, On line Journal of International Association of Engineers (IAENG) 14(2), pp. 130-134. Marcos, J.-S., Romero, R., Carrasco, D., Moreno, V., Valero, J. L. and Lafitte, M„ 2006. Implementation of new SAR change detection methods: superresolution SAR change detector. In: ESA-EUSC 2006: Image Information Mining for Security and Intelligence, Torrejon air base - Madrid (Spain). Moghaddam, H. A., Zouj, M. J. V. and Dehghani, M., 2004. Bayesian-based Despeckling in Wavelet Domain Using ”a Trous” Algorithm. In: ISPRS Congress, Istanbul 2004, Pro ceedings of Commission VII, pp. 27-13. Polidori, L., Caillault, S. and Canaud, J.-L., 1995. Change de tection in radar images: methods and operational constraints. In: Geoscience and Remote Sensing Symposium, 1995. IGARSS '95. ’Quantitative Remote Sensing for Science and Applica tions’, Florence (Italy), Vol. 2, pp. 1529-1531. Romero, R., M. J.-S., Carrasco, D., Moreno, V., Valero, J. L. and Lafitte, M., 2006. SAR Superresolution Change Detection for Security Applications. In: ESA-EUSC: Image Information Mining for Security and Intelligence 2006, EUSC, Torrejon air base - Madrid (Spain). Sveinsson, J. and Benediktsson, J., 2007. Combined wavelet and curvelet denoising of SAR images using TV segmenta tion. In: Geoscience and Remote Sensing Symposium, IGARSS 2007, Barcelona (Spain), pp. 503-506. Touzi, R., Lopes, A. and Bousquet, P., 1988. A statistical and geometrical edge detector for sar images. IEEE Transactions on Geoscience and Remote Sensing 26(6), pp. 764-773. Wright, P, Macklin, T., Willis, C. and Rye, T., 2005. Coherent Change Detection with SAR. In: European Radar Conference, EURAD 2005, Paris (France), pp. 17-20. Ying, L., Demanet, L. and Candes, E. J., 2005. 3D Discrete Curvelet Transform. In: Proc. Wavelets XI conf. 2005, Sympo sium on Optical Sciende and Technology, San Diego (CA).

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