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Proceedings Article

Spatial and temporal satellite data fusion with morphological pyramids for terrestrial surfaces survey

[+] Author Affiliations
Florence Laporterie

Ctr. d'Etudes Spatiales de la Biosphere/Ecole Nationale de la Statistique (France)

Guy Flouzat

Ctr. d'Etudes Spatiales de la Biosphere (France)

Olivier Amram

Univ. Paul Sabatier (France)

Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, 199 (January 30, 2002); doi:10.1117/12.454154
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From Conference Volume 4541

  • Image and Signal Processing for Remote Sensing VII
  • Sebastiano B. Serpico
  • Toulouse, France | September 17, 2001

abstract

Nowadays, terrestrial dynamics study is more and more often performed with the help of satellite sensors. Usually, vegetation cover surveys are performed with wide field of view sensors, because of their high temporal resolution. However, a high spatial resolution will be appreciable to distinguish each component in a landscape. We propose to create merged images combining both sensors: our fusion method is based on both theories of pyramid algorithms and mathematical morphology. Let call HR (resp. BR) the spatial resolution of the high resolution (resp. coarse) sensor image, for example SPOT 4 HRVIR and VEGETATION. The principle is : 1) To decompose the high resolution image into a low-frequency and several high-frequencies images (HFI). 2) To perform the inverse transform on the HFI images and the coarse resolution sensor data and produce the merged image. Consequently, from a temporal set of VEGETATION data and from a few HRVIR scenes, we are able to create 20m (or less) resolution synthesis data having the temporal repetitivity of the VEGETATION data set.

© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Florence Laporterie ; Guy Flouzat and Olivier Amram
"Spatial and temporal satellite data fusion with morphological pyramids for terrestrial surfaces survey", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, 199 (January 30, 2002); doi:10.1117/12.454154; http://dx.doi.org/10.1117/12.454154


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