Double-Variance Measures: A Potential Approach to Parameter Optimization of Remote Sensing Image Segmentation
文献类型：期刊论文
| 作者 | Wang, Yongji2; Tian, Zhihui2; Qi, Qingwen1,3 ; Wang, Jun4
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2021 |
| 卷号 | 14页码:2314-2326 |
| 关键词 | Image segmentation Image analysis Remote sensing Satellites Licenses Image resolution Optimization Double-variance (DV) geographic-object-based image analysis (GEOBIA) image segmentation parameter optimization unsupervised evaluation |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2021.3054638 |
| 通讯作者 | Wang, Yongji(wangyongji@zzu.edu.cn) |
| 英文摘要 | The unsupervised segmentation evaluation (USE) method has been commonly used for remote sensing segmentation parameter (SP) determinations to produce good segmentation results, due to its objectiveness and high efficiency. Existing studies have used different criteria to measure homogeneity and heterogeneity and have used certain combination strategies to form overall evaluations. However, different criteria have unique statistical characteristics. The differentiated statistical characteristics maintained in homogeneity and heterogeneity calculations may result in inherent instability in the USE results, leading to unsuitable SP selections. Moreover, few studies have focused on the simultaneous determination of a single optimal SP and multiple optimal SPs. In this article, double-variance (DV) measures were proposed for recognizing more suitable SPs. Then, two combination strategies, F-measure and local peak (LP), were applied to test the potential of using DV measures to determine a single SP and multiple SPs, respectively. The multiresolution segmentation algorithm and Gaofen-1 data were used to test the proposed method. The comparative results indicated that the DV is a more promising internal homogeneity and external heterogeneity metric for segmentation evaluation and optimal SP determination compared to conventional methods. The F-measure-based DV method could produce better overall goodness of segmentation for differently sized natural geo-objects, compared with the competing methods. The LP-based DV method could obtain multiple optimal scales that produced better segments for the identification of small, natural geo-objects to large, semantic geo-objects, compared to the competitive methods. |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000617755100006 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/160635]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Yongji |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100864, Peoples R China 2.Zhengzhou Univ, Sch Geosci & Technol, Zhengzhou 450001, 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 4.Shandong Univ Sci & Technol, Qingdao 266510, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yongji,Tian, Zhihui,Qi, Qingwen,et al. Double-Variance Measures: A Potential Approach to Parameter Optimization of Remote Sensing Image Segmentation[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:2314-2326. |
| APA | Wang, Yongji,Tian, Zhihui,Qi, Qingwen,&Wang, Jun.(2021).Double-Variance Measures: A Potential Approach to Parameter Optimization of Remote Sensing Image Segmentation.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,2314-2326. |
| MLA | Wang, Yongji,et al."Double-Variance Measures: A Potential Approach to Parameter Optimization of Remote Sensing Image Segmentation".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):2314-2326. |
入库方式： OAI收割
来源：地理科学与资源研究所
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