IGSNRR OpenIR
Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images
Wang, Yongji1,2; Qi, Qingwen1; Liu, Ying1
2018-08-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:8Pages:24
Corresponding AuthorQi, Qingwen(qiqw@igsnrr.ac.cn)
AbstractImage segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) method using the area-weighted variance (WV) and Jeffries-Matusita (JM) distance to compare two image partitions to evaluate segmentation quality. The two measures were calculated based on the local measure criteria, and the JM distance was improved by considering the contribution of the common border between adjacent segments and the area of each segment in the JM distance formula, which makes the heterogeneity measure more effective and objective. Then the two measures were presented as a curve when changing the scale from 8 to 20, which can reflect the segmentation quality in both over- and under-segmentation. Furthermore, the WV and JM distance measures were combined by using three different strategies. The effectiveness of the combined indicators was illustrated through supervised evaluation (SE) methods to clearly reveal the segmentation quality and capture the trade-off between the two measures. In these experiments, the multiresolution segmentation (MRS) method was adopted for evaluation. The proposed UE method was compared with two existing UE methods to further confirm their capabilities. The visual and quantitative SE results demonstrated that the proposed UE method can improve the segmentation quality.
Keywordimage segmentation unsupervised evaluation remote sensing area-weighted variance Jeffries-Matusita distance geographic object-based image analysis
DOI10.3390/rs10081193
WOS KeywordPARAMETER OPTIMIZATION ; SCALE PARAMETER ; MULTIRESOLUTION ; CLASSIFICATION ; LANDSCAPES ; ALGORITHMS ; WATERSHEDS ; EXTRACTION ; SELECTION ; ACCURACY
Indexed BySCI
Language英语
Funding ProjectScience and Technology Service Network Project[KFJ-EW-STS-069] ; Special Topic of Basic Work of Science and Technology[2007FY140800]
Funding OrganizationScience and Technology Service Network Project ; Special Topic of Basic Work of Science and Technology
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000443618100029
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54278
Collection中国科学院地理科学与资源研究所
Corresponding AuthorQi, Qingwen
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yongji,Qi, Qingwen,Liu, Ying. Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images[J]. REMOTE SENSING,2018,10(8):24.
APA Wang, Yongji,Qi, Qingwen,&Liu, Ying.(2018).Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images.REMOTE SENSING,10(8),24.
MLA Wang, Yongji,et al."Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images".REMOTE SENSING 10.8(2018):24.
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