Sensitivity analysis of spectral properties using multisensor image data

Knowledge about the spectral variability in a field or region of interest becomes important when it comes to defining a representative spectrum of a certain spatial extent, used for example as an endmember in spectral unmixing techniques. An approach is presented using high spatial resolution panchromatic data to assess the spectral variability of a hyperspectral dataset. The spatial variability is combined with spectral variability using spatial statistics such as auto correlation and correlation length applied to the co-registered database of the two sources. Data fusion methods and analysis applied in this manner can help to support the interpretation of high resolution spectral and spatial data in order to support future applications such as precision farming. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: http://doi.org/10.5167/uzh-98455 Published Version Originally published at: Kneubühler, Mathias; Börner, Anko; Reulke, Ralf; Schaepman, Michael E; Schläpfer, Daniel (1999). Sensitivity analysis of spectral properties using multisensor image data. In: 4th International Airborne Remote Sensing Conference and Exhibition / 21st Canadian Symposium on Remote Sensing, Ottawa, ON, Canada, 21 June 1999 24 June 1999, 107-114. SENSITIVITY ANALYSIS OF SPECTRAL PROPERTIES USING MULTISENSOR IMAGE DATA * M. Kneubuehler (1), A. Börner (1), R. Reulke (2), M. Schaepman (1) and D. Schläpfer (1) (1) Remote Sensing Laboratories (RSL), Department of Geography, University of Zurich, CH-8057 Zurich, Switzerland Phone: +41 1 635 52 46, Fax: +41 1 635 68 46, E-mail: [email protected] (2) German Aerospace Research Establishment Institute of Space Sensor Technology and Planetary Exploration, D-12484 Berlin, Germany Phone: +49 30 67055 518, Fax: +49 30 67055 529, E-mail: [email protected] Knowledge about the spectral variability in a field or region of interest becomes important when it comes to defining a representative spectrum of a certain spatial extent, used for example as an endmember in spectral unmixing techniques. An approach is presented using high spatial resolution panchromatic data to assess the spectral variability of a hyperspectral dataset. The spatial variability is combined with spectral variability using spatial statistics such as auto correlation and correlation length applied to the co-registered database of the two sources. Data fusion methods and analysis applied in this manner can help to support the interpretation of high resolution spectral and spatial data in order to support future applications such as precision farming.