Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

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:: TECHNICAL SESSION 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: DEFINITIONS AND TERMS OF REFERENCE IN DATA FUSION. L. Wald

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 corrections, etc., if necessary. The alignment problem calls upon physics, and is certainly the problem in data fusion which is the most relevant to the concerns of the remote sensing community. 5. CONCLUSIONS A new definition of the data fusion has been proposed which better fits the remote sensing domain. Data fusion should be seen as a framework, not merely as a collection of tools and means. This definition emphasizes the concepts and the fundamentals in remote sensing. Several other terms are also proposed most of which are already widely used in the scientific community, especially that dealing with information. The establishment of a lexicon or terms of reference allows the scientific community to express the same ideas using the same words, and also to disseminate their knowledge towards the industry and ’customers’ communities. Moreover it is a sine qua non condition to set up clearly the concept of data fusion and the associated formal framework. Such a framework is mandatory for a better understanding of data fusion fundamentals and its properties. It allows a better description, using similar terms clearly understood by everybody, of the potentials of synergy between remote sensing data, and accordingly their better exploitation. The problem of alignment of the information to be fused is very difficult to tackle. It is a pre-requisite to any fusion process and should be considered with great care. The remote sensing community may play a role in that domain, since it has a great experience in both the physics involved, including sensors, and the mathematical operations of sampling. Finally, the introduction of the concept of data fusion into the remote sensing domain should raise the awareness of our colleagues on the whole chain ranging from the sensor to the decision, including the management, assessment and control of the quality of the information. ACKNOWLEDGEMENTS This work has been made thanks to many fruitful discussions with several researchers and the many participants to the EARSeL - SEE working group "data fusion". I also thank Luce Castagnas, Isabelle Couloigner, Louis-François Pau, and Stelios Thomopoulos for their comments and assistance. REFERENCES Bijaoui, A., 1981. Image et segmentation. Introduction au traitement numérique des images. Masson, Paris, 242 pp. Brandstatter, G., and Sharov, A., 1998. Environmental monitoring in the high Arctic using different types of high- resolution satellite imagery. In: International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7, pp. 201- 216. Buchroithner, M., 1998. Geodata interrelations: inventory and structuring attempt of taxonomic diversity," in Proceedings of the 2nd conference "Fusion of Earth data: merging point measurements, raster maps and remotely sensed images", published by SEE/URISCA, Nice, France, pp. 11-15. Csathô, B., and Schenk, T., 1998. Multisensor data fusion for automatic scene interpretation. In: International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7, pp. 336- 341. DSTO (Defence Science and Technology Organization), 1994. Data Fusion Special Interest Group, Data fusion lexicon. Department of Defence, Australia, 7 p., 21 September 1994. Hall, D. L., and Llinas, J., 1997. An introduction to multisensor data fusion. In: Proceedings of the IEEE, Vol. 85(1), pp. 6-23. Houzelle, S., and Giraudon, G., 1994. Contribution to multisensor fusion formalization. Robotics and Autonomous Systems, Vol. 13, pp. 69-85. Kanal, L. N., and Rosenfeld, A., 1981. Progress in Pattern Recognition. North-Holland Publ., 391 pp. Klein, L. A., 1993. Sensor and Data Fusion Concepts and Applications. SPEE Optical Engineering Press, Tutorial Texts, Vol. 14, 132 p. Li, H., Manjunath, B. S., and Mitra, S. K., 1993. Multisensor image fusion using the wavelet transform. Computer Vision, Graphics, and Image Processing: Graphical Models and Image Processing, Vol. 57, pp. 235-245. Lillesand, T. M., and Kiefer, R. W., 1994. Remote Sensing and Image Interpretation. Third edition, John Wiley & Sons, 750 pp. Mangolini, M., 1994. Apport de la fusion d'images satellitaires multicapteurs au niveau pixel en télédétection et photo interprétation. Thèse de Doctorat, Université Nice - Sophia Antipolis, France, 174 p. Pau, L.-F., 1988. Sensor data fusion. Journal of Intelligent and Robotics Systems, 1, pp. 103-116. Pohl, C, and van Genderen, J. L., 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, Vol. 19(5), pp. 823- 854. Ranchin, T., Wald, L., and Mangolini, M., 1996. The ARSIS method: a general solution for improving spatial resolution of images by the means of sensor fusion. In: Proceedings of the 1 st conference "Fusion of Earth data: merging point measurements, raster maps and remotely sensed images", published by SEE/URISCA, Nice, France, pp. 53-58. Ranchin, T., and Wald, L., 1998. Sensor fusion to improve the spatial resolution of images: the ARSIS method. In:

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