Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods

Conventional approaches to accuracy assessment for land cover maps produced from remote sensing use either confusion matrices or the Kappa statistic to quantify map quality. These approaches yield global or class-specific measures of map quality by comparing classification results with independent ground-truth data. In most maps, considerable spatial variation is present in the accuracy of land cover labels that is not captured by these statistics. To date, this issue has rarely been addressed in the land cover remote sensing literature. We present a method to estimate pixel-scale land cover classification confidence using nonparametric machine learning methods. The method is based on recent theoretical developments from the domains of statistics and machine learning that explain the machine learning technique known as boosting as being equivalent to additive logistic regression. As a result, results from classification algorithms that use boosting can be assigned classification confidences based on probability estimates assigned to them using this theory. We test this approach using three different data sets. Our results demonstrate that classification errors tend to have low classification confidence while correctly classified pixels tend to have higher confidence. Thus, the method described in this paper may be used as a basis for providing spatially explicit maps of classification quality. This type of information will provide substantial additional information regarding map quality relative to more conventional quality measures and should be useful to end-users of map products derived from remote sensing.