Aspects of Standardization of Sensor and Data Fusion of Remote Sensing Data

Owing to continuous developments in technology and new requirements single sensor measurements are faced with constraints in their accuracy and thus in the applicability of their results. An innovative method of improving the geometric and radiometric data quality is the fusion of data obtained from different digital sensors. Sensor fusion means merging data obtained from different individual physical sensors to provide more comprehensive information from a single ’logical’ or ’virtual’ sensor. Airborne and spaceborne high-resolution digital cameras, laser scanners, hyperspectral systems, radar and InSAR systems have been considered in this context.Common fusion methods are resolution improvement, fusion of altitude or distance information and texture information (orthophoto generation), pan-sharpening and tracking. The derivation of orientation information from fusion of different sensors is not regarded in this paper.Assessment criteria for fusion results of data obtained from different sources have so far only been established in a few cases. Within the INS project (German Institute for Standardization DIN, 2008) the scientific bases for standardization has been developed to provide manufacturers and users with rules for the quality of the end products – also with regard to the international market. Therefore sensors and methods for achieving possible fusion products were introduced and discussed. This formed the basis for the development of a working document for a draft standard for requirements for geometric fusion methods.

[1]  A. Wehr LASER SCANNING AND ITS POTENTIAL TO SUPPORT 3D PANORAMIC RECORDING , 2005 .

[2]  Ralf Reulke,et al.  DIN 18740 "Photogrammetrische Produkte" Teil 4: Anforderungen an digitale Luftbildkameras und digitale Luftbilder , 2007 .

[3]  G. Fornaro,et al.  The Mount Etna case study: a multisensor view , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[4]  Jon Sellars,et al.  A Sensor Fusion Approach to Coastal Mapping , 2005 .

[5]  Paul D. Gader,et al.  Sensor fusion for airborne landmine detection , 2006, SPIE Defense + Commercial Sensing.

[6]  R. Hunt Colour Science : Concepts and Methods, Quantitative Data and Formulas , 1968 .

[7]  Karsten Scheibe,et al.  MFC - A Modular Line Camera for 3D World Modulling , 2008, RobVis.

[8]  Adrian Schischmanow,et al.  Monitoring traffic by optical sensors , 2005 .

[9]  Ralf Reulke,et al.  Sensorfusion für die Beschreibung der Fahrsituation mit Daten des Versuchsfahrzeugs ViewCar , 2008 .

[10]  N. Classeau,et al.  Data fusion approach for change detection in multi-temporal ERS-SAR images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[11]  Lawrence A. Klein,et al.  Sensor and Data Fusion: A Tool for Information Assessment and Decision Making , 2004 .

[12]  A Catala-Prat,et al.  Sensorfusion fuer die Beschreibung der Fahrsituation mit Daten des Versuchsfahrzeugs ViewCar / Sensor fusion for the description of driving situations with the data of the test vehicle ViewCar , 2008 .

[13]  J. E. Bare,et al.  Application of the IHS color transform to the processing of multisensor data and image enhancement , 1982 .

[14]  Ralf Reulke,et al.  Improvement of Spatial Resolution with Staggered Arrays As Used in The Airborne Optical Sensor Ads40 , 2004 .

[15]  Nicolas H. Younan,et al.  Quantitative analysis of pansharpened images , 2006 .

[16]  Aloysius Wehr,et al.  Airborne laser scanning—an introduction and overview , 1999 .

[17]  Börner Anko,et al.  MFC: a modular line camera for 3D world modulling , 2008 .

[18]  Hoang Quoc Viet,et al.  COMBINATION OF MULTISPECTRAL AND SAR REMOTE SESNING DATA FOR URBAN STUDY , 2002 .

[19]  J. Hill,et al.  A LOCAL CORRELATION APPROACH FOR THE FUSION OF REMO TE SENSING DATA WITH DIFFERENT SPATIAL RESOLUTIONS IN FORESTRY APPLICATIONS , 1999 .

[20]  K. Tachibana,et al.  A high‐level data fusion and spatial modelling system for change‐detection analysis using high‐resolution airborne digital sensor data , 2006 .

[21]  Ralf Reulke,et al.  Determination and improvement of spatial resolution of the CCD-line-scanner system ADS40 , 2006 .