A supervised and fuzzy-based approach to determine optimal multi-resolution image segmentation parameters

Abstract

Image segmentation is important for object-based classification. One of the most advanced image segmentation techniques is multi-resolution segmentation implemented by eCognition®. Multi-resolution segmentation requires users to determine a set of proper segmentation parameters through a trial-and-error process. To achieve accurate segmentations of objects of different sizes, several sets of segmentation parameters are required: one for each level. However, the trial-and-error process is time consuming and operator dependent. To overcome these problems, this paper introduces a supervised and fuzzy-based approach to determine optimal segmentation parameters for eCognition®. This approach is referred to as the Fuzzy-based Segmentation Parameter optimizer (FBSP optimizer) in this paper. It is based on the idea of discrepancy evaluation to control the merging of sub-segments to reach a target segment. Experiments demonstrate that the approach improves the segmentation accuracy by more than 16 percent, reduces the operation time from two hours to one-half hour, and is operator independent. © 2012 American Society for Photogrammetry and Remote Sensing.