Multiscale Integration Approach for Land Cover Classification Based on Minimal Entropy of Posterior Probability

Object-based land cover mapping has drawn increasing attention for its ability to overcome the salt-and-pepper problem associated with pixel-based methods by considering spatial information from neighboring regions. However, the performance of object-based classification is strongly affected by over- or undersegmented objects. The optimal scale is difficult to determine; moreover, it usually varies along with the application purpose or classification targets. Most previous efforts on scale determination based only on image information are not flexible in adapting to different classification systems; consequently, their use is not advisable. In this paper, to better consider classification targets, the information from training samples for classification is also used for determining the optimal scale based on the concept of minimal entropy of posterior probability (MEPP). The proposed MEPP method consists mainly of two stages: 1) training samples from the original pixel level are applied to classify segmented images and obtain posterior probability maps on multiple scales; and 2) the optimal object scale is determined according to the MEPP that corresponds to the minimum classification uncertainty. Experiments on high-spatial-resolution images and Landsat images confirm the superiority of the proposed MEPP method in land cover classification.