An Improved Hybrid Segmentation Method for Remote Sensing Images
文献类型：期刊论文
| 作者 | Wang, Jun1,3; Jiang, Lili3 ; Wang, Yongji2,3; Qi, Qingwen3
|
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 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). |
| WOS关键词 | WATERSHED-BASED SEGMENTATION ; MEAN-SHIFT ; EXTRACTION ; CLASSIFICATION ; VEGETATION ; SELECTION ; SCALE |
| 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 |
| 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.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。