提出了一种面向影像配准应用的仿射不变特征算法。首先选取图像中拉普拉斯算子和尺度空间微分算子同时取得最大值的点为特征点，使用仿射参数模板对特征点邻域进行重采样以补偿投影变换造成的形变。随后对采样区域求取尺度不变特征变换(SIFT)构造特征矢量。在此基础上，构造相似性判据匹配特征点，通过RANSAC(random sample consensus)算法迭代消除错配生成修正的特征点集，并精确估计变换参数。利用仿真数据，测试了所提算法在仿射变换、局部遮挡、灰度对比度变化、高斯噪声等影响因素下的性能，并用异时相卫星遥感影像验证该算法的实用价值。

An affine Invariant Feature is proposed for remote sensing image registration.The key points are extracted from image as local extrema both in scale space differential and 2D Laplacian.For each key point a series of affine parameters templates are applied to transform the neighborhood pixels，which produce several slices so that projection distortion could be mitigated.Local invariant feature descriptors are extracted from these slices using scale-space invariant features transform (SIFT).An initial matching set is obtained by matching SIFT features using a special similarity criterion.The mismatched key points are removed with greedy algorithm iteratively，and then the matching set is modified.The image transformation parameters are estimated based on this modified set.The performance of this algorithm is tested under several interferences including affine transformation，partly barrier，tonal distortion，loss of contrast，and Gaussian noise.This method is confirmed with some filed satellite images.