Probability density functions based study for identification of land cover using SAR data
Probability density functions based study for identification of land cover using SAR data
复制标题
DOI：
10.1109/icmap.2013.6733508
复制
发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
Shruti Gupta;Dharmendra Singh;P. Mishra;S. Garg
中科院分区：
文献类型：
--
作者：
Shruti Gupta;Dharmendra Singh;P. Mishra;S. Garg
全极化SAR数据具有表征和区分各种土地覆盖的能力，因为它保存了后向散射系数的幅度和相位的详细信息，这有助于区分不同的散射机制。通过极化数据的分类可以通过将其与统计信息融合来增强，但是不同类别的标记仍然是一个挑战。因此，在本文中，基于概率密度函数的方法已被提出用于识别不同类别的土地覆盖。利用极化指数信息将土地覆盖分为四类，然后对每一类应用六个概率密度函数。卡方拟合优度（GoF）测试已被用于选择最适合的密度函数为每个类。类的边界估计使用最佳拟合密度函数的尺度和位置参数。所提出的方法应用ALOS PALSAR数据，导致良好的识别城市和水域。
Fully polarimetric SAR data has the ability of characterizing and differentiating various land covers as it conserves detailed information of the amplitude and the phase of backscattering coefficient, which helps in distinguishing diverse scattering mechanisms. The classification by means of polarimetric data could be enhanced by fusing it with statistical information, but labeling of different classes is still a challenge. So, in this paper, probability density function based approach has been proposed for identification of different classes of land cover. Land cover is classified into four classes using polarimetric indices information and then six probability density functions are applied on each of the classes. Chi-Squared goodness of fit (GoF) test has been used for selecting best-fit density function for each of the classes. The boundaries of the classes were estimated using scale and location parameter of the best-fit density function. The proposed approach was applied on ALOS PALSAR data which resulted in good identification of urban and water region.