Local estimation of land cover classification quality using machine learning methods

This paper describes research to obtain local or pixel scale estimates of classification quality from classification of remote sensing data produced with nonparametric machine learning algorithms. The approach utilizes boosting, a method of improving classification accuracy using multiple iterations of a classification algorithm. Boosting has been shown to successfully improve classification accuracy, to be resistant to overfitting, and to be effective with remote sensing data. A recent statistical examination of boosting has shown that this algorithm is a form of additive logistic regression. As such, boosting can provide probabilities of class membership. These probabilities of class membership are evaluated as a measure of local classification quality. This approach is shown to be an effective predictor of classification errors and therefore, provides a means of assessing local classification quality