Study of sampling methods for accuracy assessment of classified remotely sensed data

Abstract

Accuracy assessment is an important step in the process of analyzing remote sensing data. It determines the value of the resulting data to a particular user, i.e. the information value. Remote sensing products can serve as the basis for political as well as economical decisions. Users with a variety of applications should be able to evaluate whether the accuracy of the map suits their objectives or not. In the conventional accuracy assessment an error matrix and some accuracy measures derived from it are used. An error matrix is established using some known reference data and corresponding classified data. There are various factors that affect the performance of the accuracy assessment by influencing the error matrix through out the ground truth data collection. In practice, the techniques are of little value if these effective factors are not considered. In this paper the necessity considerations for accuracy assessment including the sampling schemas and the sample size for these sampling methods are studied. Also the factors that affect selecting and applying appropriate sampling schemas and sample size are investigated. For this study numbers of synthetic images and one real image and some reference data are used. Sensitivity of the various sampling schemas has been investigated using the synthetic images and using the real image the obtained results have been confirmed. The results represent that depend on specific conditions such as type and size of the study region and object characteristics, different sampling methods and sample sizes are preferred. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.