Skip to main content

Confidence in coincidence

Buy Article:

$55.00 plus tax (Refund Policy)

We present a simple simulation scheme to derive confidence intervals for measures computed based on a coincidence matrix. Our approach is based on the conditional distributions between two categorical maps, and is a direct interpretation of how much information one map (usually the classified image) carries about the other map (usually the reference image). The simulation algorithm creates a realization of the map created by visiting each pixel and drawing a random sample from the conditional distribution of reference categories (conditioned on the category of the pixel of the classified image). Confidence intervals can be derived by repeating the simulation many times and computing the coincidence measure(s). Handling the coincidence matrix as a set of conditional distributions also allows the comparison of maps with different numbers of categories. This approach is an extension of the traditional methodology widely used in accuracy assessment of data derived from remotely sensed images. We illustrate the usage and interpretation of the approach on artificial and Canadian land cover mapping data.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: Department of Geography, University of Toronto, 3359 Mississauga Road, Mississauga, ONT L5L 1C6, Canada 2: Department of Statistics, Faculty of Social Sciences, Eötvös University, Budapest, Hungary

Publication date: 2006-03-20

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more