Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)
文献类型：期刊论文
| 作者 | Wang, Minye1; Fei, Xianyun1; Zhang, Yuanzhi2; Chen, Zhou1; Wang, Xiaoxue1; Tsou, Jin Yeu3; Liu, Dawei2; Lu, Xia1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2018-05-01 |
| 卷号 | 10期号:5页码:16 |
| 关键词 | coastal wetland vegetation feature extraction completed local binary patterns (CLBP) object-based classification |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs10050778 |
| 英文摘要 | Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. Based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pleiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images. |
| WOS关键词 | NATURE-RESERVE ; HABITAT LOSS ; CLASSIFICATION ; CONSERVATION ; ALGORITHM ; FOREST ; WATERBIRDS ; CHINA |
| 资助项目 | National Key Research and Development Program of China[2016YFB0501501] ; Natural Science Foundation of China (NSFC)[31270745] ; Natural Science Foundation of China (NSFC)[41506106] ; Lianyungang Land and Resources Project[LYGCHKY201701] ; Lianyungang Science and Technology Bureau Project[SH1629] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000435198400120 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/21954]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Fei, Xianyun; Zhang, Yuanzhi |
| 作者单位 | 1.HuaiHai Inst Technol, Sch Geomat & Marine Informat, Lianyungang 222002, Peoples R China 2.Chinese Acad Sci, Key Lab Lunar Sci & Deep Space Explorat, Natl Astron Observ, Beijing 100101, Peoples R China 3.Chinese Univ Hong Kong, Ctr Housing Innovat, Shatin, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Minye,Fei, Xianyun,Zhang, Yuanzhi,et al. Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)[J]. REMOTE SENSING,2018,10(5):16. |
| APA | Wang, Minye.,Fei, Xianyun.,Zhang, Yuanzhi.,Chen, Zhou.,Wang, Xiaoxue.,...&Lu, Xia.(2018).Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP).REMOTE SENSING,10(5),16. |
| MLA | Wang, Minye,et al."Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)".REMOTE SENSING 10.5(2018):16. |
入库方式： OAI收割
来源：国家天文台
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。