Identification of saline-alkali soil based on target decomposition of full-polarization radar data
| 第一作者： |
李洋洋 |
| 英文第一作者： |
Li, Y. Y. |
| 联系作者： |
赵凯 |
| 英文联系作者： |
Zhao, K. |
| 发表年度： |
2014 |
| 卷： |
8 |
| 摘要： |
The potential of C-band polarimetric synthetic aperture radar data for the discrimination of saline-alkali soils in the western Jilin Province, China, is shown. This area is one of the three saline-alkali landscapes in the world; the presence of saline-alkali soils severely restricts the development of local farming and limits the land use. It is extremely important to identify salinealkali landscapes accurately and effectively. Radar remote sensing is one of the most promising approaches for saline-alkali soil identification due to the sensitivity of radar data to the dielectric and geometric characteristics of objects, its weather-independent imaging capability, and its potential to acquire subsurface information, independent of the frequency band. Full polarimetric radar data from the RADARSAT-2 satellite were used. We focused on target decomposition theory and the statistical classification approach using a Wishart distribution to identify saline-alkali soils. The precise validation of the classification results is based on 129 ground sampling points. The results indicate that the polarimetric classifications using the H-(alpha) over bar method performed poorly, with Kappa values of approximately 0.29. The classification method based on Freeman-Durden decomposition showed better results, with Kappa values of approximately 0.54 and an overall accuracy of 68.22%. The best result was achieved using an input of anisotropy, with Kappa values of approximately 0.62 and an overall accuracy of 74.42%. The validity of the anisotropy approach implies that the scattering randomness of saline-alkali soil is very strong, which reflects the complex scattering characteristics of saline-alkali landscapes. Further study of the scattering characteristics of saline-alkali soil is necessary. |
| 刊物名称： |
Journal of Applied Remote Sensing |
| 参与作者： |
赵凯，郑兴明，任建华，丁艳玲，武黎黎 |
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