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Proceedings Paper

Definition of multisource prior probabilities for maximum likelihood classification of remotely sensed data
Author(s): Fabio Maselli; Claudio Conese; A. Rodolfi; Tiziana De Filippis; L. Petkov
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Paper Abstract

This paper presents an application of a methodology for the probabilistic integration of ancillary information into maximum likelihood classifications of remotely sensed data. The methodology is based on the definition of modified prior probabilities from the spectral and ancillary data sets avoiding most of the problems connected with the common uses of priors. A case study was considered concerning two rugged areas in Central Italy covered by 11 main land-use categories. Bitemporal Landsat TM scenes and the three information layers of a Digital Elevation Model (elevation, slope, aspect) were used as spectral and ancillary data. The results show that the integration of the ancillary information was fundamental for the discrimination of some classes which were practically indistinguishable only on the basis of the spectral data. The possible utilisation of the procedure within Land Information Systems is also discussed.

Paper Details

Date Published: 30 December 1994
PDF: 8 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196748
Show Author Affiliations
Fabio Maselli, IATA/CNR (Italy)
Claudio Conese, IATA/CNR (Italy)
A. Rodolfi, Ce.SIA (Italy)
Tiziana De Filippis, Ce.SIA (Italy)
L. Petkov, Ce.SIA (Italy)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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