农业工程学报2012，Vol.28Issue(19)：110-118,后插4,10.DOI:10.3969/j.issn.1002-6819.2012.19.015
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
Registration algorithm based on nonsubsampled contour transform and morphological shrink operator for multi-source images
摘要
Abstract
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 nonsubsampted 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.关键词
遥感/算法/数学变换/多源遥感影像配准/非子采样轮廓变换/形态收缩算子/归一化互信息Key words
remote sensing/ algorithms/ mathematical transformations/ multi-source remote sensing image registration/ nonsubsampled contourlet transform/ morphological shrink operator/ normalized mutual information分类
信息技术与安全科学引用本文复制引用
王瑞瑞,马建文,石伟,黄华国..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[J].农业工程学报,2012,28(19):110-118,后插4,10.基金项目
中央高校基本科研业务费专项资金(BLYX200917),北京林业大学青年科技启动基金"基于选择性视觉注意机制的马尾松智能识别模型研究"资助(编号:BLX2011003). （BLYX200917）
