Rocky Mountain Research Station

Title: Calibration of remotely sensed proportion or area estimates for misclassification error

Author: Czaplewski, Raymond L.; Catts, Glenn P.

Date: 1992

Source: Remote Sensing of Environment. 39(1): 29-43

Description: Classifications of remotely sensed data contain misclassification errors that bias areal estimates. Monte Carlo techniques were used to compare two statistical methods that correct or calibrate remotely sensed areal estimates for misclassification bias using reference data from an error matrix. The inverse calibration estimator was consistently superior to the classical estimator using a simple random sample of reference plots. The effects of sample size of reference plots, detail of the classification system, and classification accuracy on the precision of the inverse estimator are discussed. If reference plots are a simple random sample of the study area, then a total sample size of 500-1000 independent reference plots is recommended for calibration.

Keywords: remote sensing; calibration; misclassification error

View and Print this Publication (1.2 MB)

Publication Notes: 

• We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
• This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

 Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

Citation

Czaplewski, Raymond L.; Catts, Glenn P.  1992.  Calibration of remotely sensed proportion or area estimates for misclassification error.   Remote Sensing of Environment. 39(1): 29-43.