Paper Info
Title
Improvement Of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria
Abstract
Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.
Year
DOI
Venue
2021
10.3390/rs13142782
REMOTE SENSING
Keywords
DocType
Volume
image segmentation, region merging, segmentation quality optimization, merging criteria
Journal
13
Issue
Citations 
PageRank 
14
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Haoyu Wang100.34
Zhanfeng Shen200.34
Zihan Zhang33610.42
Zeyu Xu400.34
Shuo Li500.34
Shuhui Jiao600.34
Yating Lei700.34