Statistical integration of ancillary data into an advanced maximum likelihood classifier

Claudio Conese; Fabio Maselli; Tiziana De Filippis; Maurizio Romani

doi:10.1117/12.182799

17 August 1994 Statistical integration of ancillary data into an advanced maximum likelihood classifier

Claudio Conese, Fabio Maselli, Tiziana De Filippis, Maurizio Romani

Proceedings Volume 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision; (1994) https://doi.org/10.1117/12.182799
Event: Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, 1994, Munich, Germany

Abstract

The inclusion of prior probabilities derived from the frequency histograms of the training sets has already been demonstrated to significantly improve the performance of a maximum likelihood classifier. Based on the same principles, a method is presently proposed to integrate the information of ancillary data layers (morphology, pedology, etc.) into the classification process. The statistical basis of this probabilistic approach is first described. A case study is then illustrated concerning a rugged area in Tuscany, (central Italy) sensed by bitemporal Landsat thematic mapper (TM) scenes. Ground references of nine cover categories were collected and digitized together with four ancillary data layers (elevation, slope, aspect, and soils). A maximum likelihood classification with nonparametric priors based only on the TM scenes was first tested, yielding a Kappa accuracy of 0.744. The ancillary data were integrated into the modified classifier, with notable increases in classification accuracy (up to Kappa equals 0.910). It is concluded that the utility of such an approach must be evaluated in relation to the characteristics of the landscape and the satellite imagery considered.

Citation Download Citation

Claudio Conese, Fabio Maselli, Tiziana De Filippis, and Maurizio Romani "Statistical integration of ancillary data into an advanced maximum likelihood classifier", Proc. SPIE 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, (17 August 1994); https://doi.org/10.1117/12.182799

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available