Segmentation of SAR images using multitemporal information
Segmentation of SAR images using multitemporal information
复制标题
使用多时相信息分割 SAR 图像
DOI：
10.1049/ip-rsn:20030751
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
K. Ouchi
中科院分区：
文献类型：
--
作者：
G. Davidson;K. Ouchi
SAR分割的最大似然方法有可能保持单像素精度，而不需要启发式决策。通常，概率测量用于合并各个区域，而无需假设基础横截面的任何先验知识。然而，对于合理的多时间场景，可以从随时间变化的横截面获得相当多的信息。给出了一个例子，其中该信息可以通过初始分类来提取。然后，它示出了如何可以修改分割方案，通过估计的多时间的底层类分布，将这些信息。使用单看雷达卫星数据在8米的分辨率，它演示了如何最后的部分人口可以显着减少。从地面调查数据和高分辨率AirSAR图像的比较，分割的结构质量得到改善。
The maximum likelihood method of SAR segmentation has the potential to retain single pixel accuracy without requiring heuristic decisions. Normally a probabilistic measure is used to merge individual regions without assuming any prior knowledge for the underlying cross-sections. However, for a reasonable multitemporal scene there may be considerable information available from the varying cross-sections over time. An example is given where this information can be extracted by an initial classification. It is then shown how the segmentation scheme can be modified to incorporate this information via an estimate of the multitemporal underlying class distributions. Using single-look Radarsat data at 8 m resolution, it is demonstrated how the final segment population can be significantly reduced. From a comparison with ground survey data and a high-resolution AirSAR image, the structural quality of the segmentation is shown to be improved.