基于改进证据理论的多时相微波遥感图像融合及在城区地表变化检测中的应用

该文发展证据理论融合算法，采用不同时相的微波遥感图像检测复杂城市区域地表的变化信息。首先通过综合考虑证据本身的确信度和证据对辨别框架中子集的平均支持度进行证据间的加权合成，改进证据理论对证据的合成，提高其可靠性；然后提取不同时相图像间的散射幅度的对比度和概率密度分布函数在皮尔逊图中的欧式距离，两种特征参数代表了像元级和区域级不同空间尺度下微波遥感图像中关于地表变化的信息；最后将改进的证据理论用于两特征之间的融合处理，得到地表的变化信息。为了实验和验证该文的方法，选择上海市陆家嘴地区不同时相的微波遥感图像，进行地表的变化检测，得到较好的结果。

Data Fusion of Multi-Temporal SAR Remote Sensing with Improved D-S Algorithm and Application to Change Detection of Urban Terrain

Dempster-Shafer (D-S) algorithm is improved to fuse different features of multi-temporal SAR images to detect change detection of urban areas. Firstly, Dempster-Shafer is developed by not only considering the certainty of the evidence, but also considering the average support of the evidence to different subsets in the assignment framework so that it can give more reliable combination result. Secondly, amplitude ratio feature and Euclid distance of the probability density distribution function in Pearson graph from different temporal SAR images are extracted to present change feathers in different scales. Finally, the improved Dempster-Shafer algorithm is applied to fuse the two different features to detect change information of SAR images. An example of the Shanghai Lujiazui area using the ERS-2 SAR image well demonstrates the accuracy of the improved fusion algorithm.