Self-adaptive segmentation of satellite images based on a weighted aggregation approach

Image segmentation is a decisive process in object-based image analysis, while the uncertainty of segmentation scales can significantly influence the results. To resolve this issue, this study proposes a Self-Adaptive Segmentation (SAS) method which bridges the gap between the inherent scale and segmentation scale of each object. Firstly, SAS is defined as a variable-scale segmentation approach, aiming at generating local optimum results. It is then implemented based on Segmentation by Weighted Aggregation (SWA) method and optimized by selfhood scale estimation technique. Secondly, two images derived from WorldView-2 (an urban area) and Landsat-8 (a farmland area) are employed to verify the effectiveness and adaptability of this method. Thirdly, it is further compared with SWA and mean shift method by reference to two evaluations, including an unsupervised evaluation, i.e., Global Score (GS), and a supervised one named Geometry-based comparison. The experimental results indicate the SAS method presented in this study is effective in solving the scale issues in multiscale segmentations. It shows higher accuracy than SWA through assigning an appropriate segmentation scale for each object. Moreover, the segmentation results achieved by SAS method in both study areas are more accurate than those by mean shift method, which demonstrates the stable performance of SAS method across diverse scenes and data sets. This advantage is more obvious in farmland area than in urban scene, according to the accuracy assessment results.