Scene-Specific Unsupervised Segmentation Parameter Optimization for High-Resolution Remote Sensing Images

The primary challenge in image segmentation arises from the scale selection issue induced by the complex structure of scenes portrayed as an image. To solve this problem, this study developed a novel framework named scene-specific unsupervised segmentation parameter optimization to conduct multiscale segmentation. Images were firstly divided into different partitions based on the complexity of scene structures, where the structural complexity was measured using an extended structural similarity index. Subsequently, parameter optimization was implemented separately within each image partition. The results indicated significant variations in the estimated optimal segmentation scales across different partitions, following the corresponding scene structural complexity within each partition. Moreover, the accuracy evaluation results demonstrated the effectiveness of our methodology in segmenting geographic objects with diverse geometric and spectral features, significantly improving the overall segmentation quality.