摘要
提出了一种新型全自动稳健的遥感图像配准算法。首先,在图像二维平面空间和尺度空间中同时检测局部极值作为特征点,并在特征点邻域提取局部不变特征描述子——尺度不变特征变换(SIFT)。然后,利用距离测度进行SIFT特征匹配得到初步的匹配集合。最后,运用稳健的随机采样一致性(RANSAC)算法将匹配点集划分为内点和外点,在内点域上精确地估计出图像变换模型。实验利用仿真数据测试了SIFT特征的可重复性和可匹配性,利用卫星图像验证了该自动配准算法的有效性和稳健性。
A novel automatic and robust remote sensing image registration method is proposed. At first, the feature points are identified as local extrema both in 2D-space and scale space of the image, and the local invariant feature descriptors are extracted in the neighborhoods of the feature points, named as scale invariant feature transform （SIFT）. Then an initial matching set is obtained by matching the SIFt features based on the distance gather. At last, the matching point set is divided into inner and outer point using robust random sample consensus （RANSAC） algorithm, and the image transform model is accurately estimated by the inner sub-set. The repeatability and matchability of SIFT features are tested by the simulated experiment. The validity and the robustness is validated by the experiment with satellite image of the proposed method.
出处
《传感器与微系统》
CSCD
北大核心
2009年第10期12-15,共4页
Transducer and Microsystem Technologies
关键词
尺度不变特征变换
内点外点
随机采样一致性
自动配准
scale invariant fenrure transform（SIFF）
inliers and outliers
random sample consensus（RANSAC）
automatic registration
