结合非子采样轮廓变换和形态收缩算子的多源遥感影像配准

针对多源遥感影像自动配准中难以提取大量同名特征点的问题,提出了一种结合非子采样轮廓变换和形态收缩算子的自动配准算法。结合非子采样轮廓变换和形态收缩算子的特征提取算法能够克服角度和尺度偏差,在多方向、多尺度空间精确提取强边缘上的关键结构特征点;基于低频波段的归一化互信息匹配算法和三角形一致检验算法能够提取到大量高可靠性的同名特征点对,保证了多源遥感影像的高精度配准。文中选取角度和尺度偏差显著的SPOT-5(P)和ASTER影像组合进行试验,结果证明以上算法能够检测到大量分布均匀的同名特征点对,配准模型精度趋近于1个像元。该研究可为多源遥感数据的融合和目标识别提供前提条件。

Registration algorithm based on nonsubsampled contour transform and morphological shrink operator for multi-source images

It is difficult to extract the corresponding features from the multi-source images in automatic registration between them. Aiming to this problem, a new registration method based on the nonsubsampled contourlet transform (NSCT) and morphological shrink operator (MSO) was proposed. The feature extraction method based on NSCT_MSO can reduce the differences in angle and scale, and extract key structural feature points in multi-scale and multi-directional space. The feature matching method based on normalized mutual information computed from the low frequency band and the triangular consistency inspect method can extract a considerable number of corresponding feature points with even distribution, which ensure a high accuracy for the registration between multi-source images. The performance of the proposed algorithm was demonstrated and validated by experiments on SPOT-5(P) and ASTER images with considerable differences in angle and scale. The experimental results indicate that many corresponding feature points with even distribution can be obtained with the new algorithm and the accuracy of registration model is close to 1 pixel. The research can provide a basis for image fusion and object recognition.