A Novel Multi-radius Neighborhood Rough Set Weighted Feature Extraction Method for Remote Sensing Image Classification | |
Chen Tieqiao1,2,3; Liu Jiahang1; Zhu Feng1,2,3; Wang Yihao1; Liu Jia1; Chen Jie4 | |
刊名 | Geomatics and Information Science of Wuhan University |
2018-02 | |
卷号 | 43期号:2页码:311-317 |
关键词 | Neighborhood Rough Set Model Multi-radius Neighborhood Rough Set Feature Extraction High Spatial Resolution Remote Sensing Image Object-oriented Classification |
ISSN号 | 1671-8860 |
DOI | 10.13203/j.whugis20150290 |
其他题名 | 适用于遥感分类的多邻域粗糙集加权特征提取方法 |
产权排序 | 1 |
英文摘要 | The neighborhood rough set model can be effective for keeping or even improving classification accuracy.This model however,still has some disadvantages as it has low stability in classification precision,requires repeated neighborhood radius adjustments,and cannot realize automatic feature extraction.In order to solve these problems,this paper presents a multi-radius neighborhood rough set weighted feature extraction method for high resolution remote sensing image classification.The neighborhood rough set model was used to extract texture and spectrum features of image by setting gradually increasing radius with equally spaced steps,as a result effective subset features under different radius were obtained.The presence probablity of each feature under all the different radius was calculated,each feature was endowed with weight by its presence probability,so the final weighted features of image were acquired.The newly obtained features were applied to image classification using a support vector machine.Experiments on QuickBird images demonstrate that the proposed method can provide better classification results.Compared with other state-off-art neighborhood rough set model with effective radius,the overall accuracy exceeded about 3.88% while the Kappa coefficient exceeded about 5.16%.A classification experiment on a GeoEye-1 image also showed the effectiveness of the proposed method.All the classification experiment results show that the proposed method can improve classification precision and automation of high resolution remote sensing images. |
学科主题 | Geology |
语种 | 中文 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/30097] |
专题 | 西安光学精密机械研究所_遥感与智能信息系统研究中心 |
通讯作者 | Liu Jiahang |
作者单位 | 1.Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an, 710119, China 2.University of Chinese Academy of Sciences, Beijing, 100049, China 3.Key Laboratory of Lunar and Deep Space Exploration, Beijing 1000012, China 4.Department of Geo-informatics, Central South University, Changsha 410083, China |
推荐引用方式 GB/T 7714 | Chen Tieqiao,Liu Jiahang,Zhu Feng,et al. A Novel Multi-radius Neighborhood Rough Set Weighted Feature Extraction Method for Remote Sensing Image Classification[J]. Geomatics and Information Science of Wuhan University,2018,43(2):311-317. |
APA | Chen Tieqiao,Liu Jiahang,Zhu Feng,Wang Yihao,Liu Jia,&Chen Jie.(2018).A Novel Multi-radius Neighborhood Rough Set Weighted Feature Extraction Method for Remote Sensing Image Classification.Geomatics and Information Science of Wuhan University,43(2),311-317. |
MLA | Chen Tieqiao,et al."A Novel Multi-radius Neighborhood Rough Set Weighted Feature Extraction Method for Remote Sensing Image Classification".Geomatics and Information Science of Wuhan University 43.2(2018):311-317. |
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