面向变化检测的遥感影像弹性配准方法

Elastic Registration of Remote Sensing Images for Change Detection

对于遥感载荷技术指标差异、观测角度、时相、地形起伏等内外部因素造成的几何畸变,采用全局配准方法制约着影像配准和变化检测精度的提高。提出一种基于加速抗差特征（speed up robust feature,SURF）算法的全局匹配和像元局部配准模型相结合的弹性配准方法,以不同时相遥感影像的差值特征影像各像元正态分布密度函数构建像元局部参数解算权重,缓减不同时相影像中辐射亮度差异较大的像元对局部配准模型参数解算的影响,采用城市典型区域遥感影像进行实验,结果表明该方法影像配准精度（包括地形起伏区域）优于1个像元,弹性配准算法的适用性和运算速度有一定的提高。

Algorithms based on global rigid model can not resolve local geometric distortion problems caused by internal and external factors such as different remote sensing payloads,observation angles,times,and topography.Gobal algorithms restrict accuracy improvemenst in automatic registration and change detection in remote sensing images.In this paper,we present an elastic registration method based on a preliminary global speed up robust feature（SURF）affine registration method,local translation,and smoothing models.We constructed the weight function with normal density function of each pixel in the difference image to weaken errors of local translation paramters,caused by different radiation intensities of pixels.Experiments using urban area data show that this method of image registration（including topography area）is more accuracate than a pixel,and is applicable and effective for change detection.