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Proceedings Paper

Partially supervised classification of multisource data
Author(s): Diego Fernandez-Prieto; Olivier Arino
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Paper Abstract

A novel data fusion approach to partially supervised classification problems is presented, which allows a specific land-cover class of interest to be mapped by using only training samples belonging to such class. This represents a significant operational advantage in many application domains where end-users require information products for the monitoring of a specific or few land cover classes (e.g., forestry, urban monitoring) of interest. The proposed technique overcomes one of the main methodological drawbacks of this type of problems: i.e., the lack of prior knowledge on the statistics of the unknown classes present in the scene under consideration. Experiments carried out on a multisource data set demonstrate the validity of the proposed technique.

Paper Details

Date Published: 13 March 2003
PDF: 9 pages
Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); doi: 10.1117/12.463173
Show Author Affiliations
Diego Fernandez-Prieto, European Space Agency/ESRIN (Italy)
Olivier Arino, European Space Agency/ESRIN (Italy)


Published in SPIE Proceedings Vol. 4885:
Image and Signal Processing for Remote Sensing VIII
Sebastiano B. Serpico, Editor(s)

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