Coastal zone classification with fully polarimetric SAR imagery
文献类型：期刊论文
| 作者 | Gou, Shuiping1; Li, Xiaofeng1; Yang, Xiaofeng1 |
| 刊名 | IEEE Geoscience and Remote Sensing Letters
 |
| 出版日期 | 2016 |
| 卷号 | 13期号:11页码:1616-1620 |
| 关键词 | SKY RADIANCE MEASUREMENTS OPTICAL-PROPERTIES PHOTOPOLARIMETRIC MEASUREMENTS BIDIRECTIONAL REFLECTANCE SATELLITE-OBSERVATIONS INVERSION ALGORITHM INFORMATION-CONTENT SOLAR IRRADIANCE POLARIZATION SUN |
| 通讯作者 | Li, Xiaofeng (xiaofeng.li@noaa.gov) |
| 英文摘要 | Classifying different types of land cover in coastal zones using synthetic aperture radar (SAR) imagery is a challenge due to the fact that many types of coastal zone have similar backscattering characteristics. In this letter, we propose an unsupervised method based on a three-channel joint sparse representation (SR) classification with fully polarimetric SAR (PolSAR) data. The proposed method utilizes both texture and polarimetric feature information extracted from the HH, HV, and VV channels of a SAR image. The texture features are extracted by applying a wavelet transform to a SAR image, and then sparsely represented based on the correlation among the three channels. The polarimetric features, i.e., the scattering entropy and scattering angle from the Hα model, are also sparsely represented. A joint SR algorithm using both texture and polarimetric features is constructed to establish target dictionaries. An orthogonal matching pursuit algorithm is then used to calculate sparse coefficients. Hybrid coefficients are inputted to the kernel support vector machine for a fully PolSAR image classification. We applied the proposed algorithm to an Advanced Land Observing Satellite-2 L-band SAR image acquired in the Yellow River Delta, China. The classified land types are validated against the official survey map. The algorithm performs well in distinguishing six coastal land-use types. A comparison study is also conducted to show that proposed algorithm outperforms two commonly used classification methods. © 2004-2012 IEEE. |
| 学科主题 | Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20163502763773 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39499]  |
| 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. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 2.710071, China 3. Global Science and Technology at National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, College Park 4.MD 5.20740, United States 6. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 7.100101, China |
| 推荐引用方式 GB/T 7714 | Gou, Shuiping,Li, Xiaofeng,Yang, Xiaofeng. Coastal zone classification with fully polarimetric SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters,2016,13(11):1616-1620. |
| APA | Gou, Shuiping,Li, Xiaofeng,&Yang, Xiaofeng.(2016).Coastal zone classification with fully polarimetric SAR imagery.IEEE Geoscience and Remote Sensing Letters,13(11),1616-1620. |
| MLA | Gou, Shuiping,et al."Coastal zone classification with fully polarimetric SAR imagery".IEEE Geoscience and Remote Sensing Letters 13.11(2016):1616-1620. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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