351. Segmentation of airborne hyperspectral images by integrating multi-level data fusion
- Author
-
M.C. Mouchot, Marc Lennon, Gregoire Mercier, Basel Solaiman, Laurence Hubert-Moy, Télécom Bretagne, Bibliothèque, Département Image et Traitement Information (ITI), and Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)
- Subjects
Pixel ,Computer science ,business.industry ,Hyperspectral imaging ,Pattern recognition ,Image segmentation ,Sensor fusion ,Fuzzy logic ,Segmentation ,Computer vision ,Artificial intelligence ,Image sensor ,business ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Image resolution ,[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing - Abstract
International audience; This paper deals with the extraction of the hedgerow and copse network from hyperspectral images acquired with the Compact Airborne Spectrographic Imager (CASI). The strategy of segmentation integrates several levels of data fusion allowing a decision to be taken concerning the membership of each pixel to the hedgerow and copse network from the large set of original data. The first level leads to quantifying the membership of each pixel to specific features of the network. It includes data fusion based on physical properties, geometric context-dependent fuzzy fusion with an original consistency measure and the geometric fusion of decisions. The second level is a fuzzy fusion of methods allowing the membership of each pixel to the network to be quantified. Finally, the third level involves post-processing the data with a context-dependent fusion of decisions to obtain the final map of the hedgerow and copse network.
- Published
- 2000