IGSNRR OpenIR
Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method
Wang, Yongji1,2,3; Meng, Qingyan2,4; Qi, Qingwen1; Yang, Jian2,4; Liu, Ying4,5
2018-05-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:5Pages:26
Corresponding AuthorMeng, Qingyan(mengqy@radi.ac.cn) ; Qi, Qingwen(qiqw@igsnrr.ac.cn)
AbstractImage segmentation is an important process and a prerequisite for object-based image analysis, but segmenting an image into meaningful geo-objects is a challenging problem. Recently, some scholars have focused on hybrid methods that employ initial segmentation and subsequent region merging since hybrid methods consider both boundary and spatial information. However, the existing merging criteria (MC) only consider the heterogeneity between adjacent segments to calculate the merging cost of adjacent segments, thus limiting the goodness-of-fit between segments and geo-objects because the homogeneity within segments and the heterogeneity between segments should be treated equally. To overcome this limitation, in this paper a hybrid remote-sensing image segmentation method is employed that considers the objective heterogeneity and relative homogeneity (OHRH) for MC during region merging. In this paper, the OHRH method is implemented in five different study areas and then compared to our region merging method using the objective heterogeneity (OH) method, as well as the full lambda-schedule algorithm (FLSA). The unsupervised evaluation indicated that the OHRH method was more accurate than the OH and FLSA methods, and the visual results showed that the OHRH method could distinguish both small and large geo-objects. The segments showed greater size changes than those of the other methods, demonstrating the superiority of considering within- and between-segment heterogeneity in the OHRH method.
Keywordimage segmentation region merging within- and between-segment heterogeneity watershed transformation geographic object-based image analysis (GEOBIA)
DOI10.3390/rs10050781
WOS KeywordPARAMETER SELECTION ; SATELLITE IMAGES ; MULTIRESOLUTION ; INFORMATION ; WATERSHEDS ; EXTRACTION ; BOUNDARY ; AREAS
Indexed BySCI
Language英语
Funding ProjectKey International Special Projects for Scientific and Technological Innovation Cooperation[2016YFE0122200] ; National Key Research and Development Program of China[2016YFC0802500] ; Hainan Province Natural Science Foundation Innovative Research Team: Study on Urban Green Landscape Pattern Remote Sensing Evaluation based on Lidar and Multispectral Data[2017CXTD015] ; Technology of Land Surface Coverage Monitoring and Rapid Detection of Surface Cover Change of GF-6 Satellite Wide Range Camera, National Natural Science Foundation of China[41471310] ; China National Key S and T project of high-resolution earth observation system[30-Y20A07-9003-17/18]
Funding OrganizationKey International Special Projects for Scientific and Technological Innovation Cooperation ; National Key Research and Development Program of China ; Hainan Province Natural Science Foundation Innovative Research Team: Study on Urban Green Landscape Pattern Remote Sensing Evaluation based on Lidar and Multispectral Data ; Technology of Land Surface Coverage Monitoring and Rapid Detection of Surface Cover Change of GF-6 Satellite Wide Range Camera, National Natural Science Foundation of China ; China National Key S and T project of high-resolution earth observation system
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000435198400123
PublisherMDPI
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54640
Collection中国科学院地理科学与资源研究所
Corresponding AuthorMeng, Qingyan; Qi, Qingwen
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Sanya Inst Remote Sensing, Sanya 572029, Peoples R China
5.South China Normal Univ, Coll Geog, Guangzhou 510631, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yongji,Meng, Qingyan,Qi, Qingwen,et al. Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method[J]. REMOTE SENSING,2018,10(5):26.
APA Wang, Yongji,Meng, Qingyan,Qi, Qingwen,Yang, Jian,&Liu, Ying.(2018).Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method.REMOTE SENSING,10(5),26.
MLA Wang, Yongji,et al."Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method".REMOTE SENSING 10.5(2018):26.
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