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- Title
A Supervised and Fuzzy-based Approach to Determine Optimal Multi-resolution Image Segmentation Parameters.
- Authors
Hengjian Tong; Maxwell, Travis; Yun Zhang; Vivek Dey
- 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.
- Subjects
IMAGE segmentation; DIGITAL images; FUZZY systems; OPTICAL resolution; DISCREPANCY theorem
- Publication
Photogrammetric Engineering & Remote Sensing, 2012, Vol 78, Issue 10, p1029
- ISSN
0099-1112
- Publication type
Article
- DOI
10.14358/PERS.78.10.1029