Multi-scale Segmentation of High-Spatial Resolution Remote Sensing Images Using Adaptively Increased Scale Parameter

Abstract

Abstract The adaptively increased scale parameter (AISP) strategy is proposed to control multi-scale segmentation based on region growing methods. AISP strategy contains a set of gradually increased scale parameters to produce nested multi-scale segments. Instead of independently assigning the set of scale parameters ahead of segmentation, the contribution of this study is to dynamically determine scale parameters during segmentation procedure, making scale parameters adaptive to specific images and cover meaningful segmentation scales. Furthermore, the effectiveness of gradually increased scale parameters on segmentation accuracy is analyzed, which gives a thorough understanding to local-oriented region growing methods. The experimental results on a set of high-resolution images proved the effectiveness of AISP on controlling multi-scale segmentation. AISP holds the application potential for object-based analysis of high-resolution images.