An Improved Hybrid Segmentation Method for Remote Sensing Images

doi:10.3390/ijgi8120543

	An Improved Hybrid Segmentation Method for Remote Sensing Images
	Wang, Jun 2,3; Jiang, Lili 2; Wang, Yongji 1,2; Qi, Qingwen 2
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2019-12-01
卷号	8 期号:12 页码:23
关键词	segmentation watershed GF-1 images fast lambda-schedule common boundary length penalty
DOI	10.3390/ijgi8120543
通讯作者	Jiang, Lili(jiangll@igsnrr.ac.cn)
英文摘要	Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers' attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran's index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).
资助项目	National Key Research and Development Program of China[2017YFB0503500] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040402]
WOS关键词	WATERSHED-BASED SEGMENTATION ; MEAN-SHIFT ; EXTRACTION ; CLASSIFICATION ; VEGETATION ; SELECTION ; SCALE
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000518041800022
资助机构	National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/133039]
专题	中国科学院地理科学与资源研究所
通讯作者	Jiang, Lili
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China
推荐引用方式 GB/T 7714	Wang, Jun,Jiang, Lili,Wang, Yongji,et al. An Improved Hybrid Segmentation Method for Remote Sensing Images[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(12):23.
APA	Wang, Jun,Jiang, Lili,Wang, Yongji,&Qi, Qingwen.(2019).An Improved Hybrid Segmentation Method for Remote Sensing Images.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,8(12),23.
MLA	Wang, Jun,et al."An Improved Hybrid Segmentation Method for Remote Sensing Images".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 8.12(2019):23.