Definition of multisource prior probabilities for maximum likelihood classification of remotely sensed data

Fabio Maselli; Claudio Conese; A. Rodolfi; Tiziana De Filippis; L. Petkov

doi:10.1117/12.196748

30 December 1994 Definition of multisource prior probabilities for maximum likelihood classification of remotely sensed data

Fabio Maselli, Claudio Conese, A. Rodolfi, Tiziana De Filippis, L. Petkov

Author Affiliations +

Proceedings Volume 2315, Image and Signal Processing for Remote Sensing; (1994) https://doi.org/10.1117/12.196748
Event: Satellite Remote Sensing, 1994, Rome, Italy

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.

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Fabio Maselli, Claudio Conese, A. Rodolfi, Tiziana De Filippis, and L. Petkov "Definition of multisource prior probabilities for maximum likelihood classification of remotely sensed data", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196748

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