Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data
文献类型：期刊论文
| 作者 | Xie, Lei1; Zhang, Hong1; Wang, Chao1; Chen, Fulong1 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2016 |
| 卷号 | 50页码:10-25 |
| 关键词 | SYNTHETIC-APERTURE RADAR SOUTH FLORIDA WETLANDS ALTIMETRY IMAGERY INTERFEROMETRY PATTERNS STORAGE MODEL |
| 通讯作者 | Zhang, H (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. |
| 英文摘要 | This paper presents a novel method for supervised water-body extraction and water-body types identification from Radarsat-2 fully polarimetric (FP) synthetic aperture radar (SAR) data in complex urban areas. First, supervised water-body extraction using the Wishart classifier is performed, and the false alarms that are formed in built-up areas are removed using morphological processing methods and spatial contextual information. Then, the support vector machine (SVM), the classification and regression tree (CART), TreeBagger (TB), and random forest (RF) classifiers are introduced for water-body types (rivers, lakes, ponds) identification. In SAR images, certain other objects that are misclassified as-water are also considered in water-body types identification. Several shape and polarimetric features of each candidate Water-body are used for identification. Radarsat-2 PolSAR data that were acquired over Suzhou city and Dongguan city in China are used to validate the effectiveness of the proposed method, and the experimental results are evaluated at both the object and pixel levels. We compared the water-body types classification results using only shape features and the combination of shape and polarimetric features, the experimental results show that the polarimetric features can eliminate the misclassifications from certain other objects like roads to water areas, and the increasement of classification accuracy embodies at both the object and pixel levels. The experimental results show that the proposed methods can achieve satisfactory accuracies at the object level [89.4% (Suzhou), 95.53% (Dongguan)] and the pixel level [96.22% (Suzhou), 97.95% (Dongguan)] for water-body types classification, respectively. (C) 2016 Elsevier B.V. All rights reserved. |
| 学科主题 | Remote Sensing |
| 类目[WOS] | Remote Sensing |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000375819200002 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39171]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Lei,Zhang, Hong,Wang, Chao,et al. Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2016,50:10-25. |
| APA | Xie, Lei,Zhang, Hong,Wang, Chao,&Chen, Fulong.(2016).Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,50,10-25. |
| MLA | Xie, Lei,et al."Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 50(2016):10-25. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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