基于压缩感知的遥感影像弹性配准方法
发布时间：2018-12-09 13:51
【摘要】：针对遥感影像由于载荷类型、观测角度、地形起伏等内外部因素造成的影像局部几何畸变,而基于全局配准方法制约着影像配准精度的提高,基于像元的弹性配准方法可大幅提升遥感影像的配准精度,但是存在运算效率这一瓶颈等问题,该文利用像元弹性配准参数的稀疏性,提出一种基于压缩感知的弹性配准方法。通过对遥感影像像元梯度幅值响应较强的点进行随机抽样,形成观测样本点集,采用弹性配准局部参数解算模型求解样本点平移参数;通过压缩感知稀疏重构算法重构影像各像元平移参数。实验表明,在配准精度差异较小和一定的参数设置条件下,该方法可显著提高弹性配准运算速度。
[Abstract]:Aiming at the local geometric distortion of remote sensing image caused by internal and external factors such as load type, observation angle, topography fluctuation and so on, the improvement of image registration accuracy is restricted by global registration method. The elastic registration method based on pixel can greatly improve the registration accuracy of remote sensing image, but there are some problems such as the bottleneck of computing efficiency. This paper proposes an elastic registration method based on compressed sensing based on the sparsity of pixel elastic registration parameters. Through random sampling of the points with strong gradient amplitude response of image pixel, the observation sample point set is formed, and the translation parameter of the sample point is solved by using the elastic registration local parameter solution model. The translational parameters of each pixel are reconstructed by compressed sparse reconstruction algorithm. The experimental results show that this method can significantly improve the speed of elastic registration under the condition of little difference in registration accuracy and certain parameter setting.
【作者单位】： 中国科学院电子学研究所;中国科学院光电研究院;中科九度(北京)空间信息技术有限责任公司;
【基金】：中国科学院科技服务网络计划项目(KFJ-EW-STS-046) 国家高技术研究发展计划项目(2014AA09A511) 高分辨率对地观测系统重大专项(E0303/1315/05)
【分类号】：TP751
本文编号：2369459
[Abstract]:Aiming at the local geometric distortion of remote sensing image caused by internal and external factors such as load type, observation angle, topography fluctuation and so on, the improvement of image registration accuracy is restricted by global registration method. The elastic registration method based on pixel can greatly improve the registration accuracy of remote sensing image, but there are some problems such as the bottleneck of computing efficiency. This paper proposes an elastic registration method based on compressed sensing based on the sparsity of pixel elastic registration parameters. Through random sampling of the points with strong gradient amplitude response of image pixel, the observation sample point set is formed, and the translation parameter of the sample point is solved by using the elastic registration local parameter solution model. The translational parameters of each pixel are reconstructed by compressed sparse reconstruction algorithm. The experimental results show that this method can significantly improve the speed of elastic registration under the condition of little difference in registration accuracy and certain parameter setting.
【作者单位】： 中国科学院电子学研究所;中国科学院光电研究院;中科九度(北京)空间信息技术有限责任公司;
【基金】：中国科学院科技服务网络计划项目(KFJ-EW-STS-046) 国家高技术研究发展计划项目(2014AA09A511) 高分辨率对地观测系统重大专项(E0303/1315/05)
【分类号】：TP751
【相似文献】
相关期刊论文 前1条
1 张红颖;张加万;孙济洲;杨甲东;;基于层次B样条的医学图像弹性配准方法[J];天津大学学报;2007年01期
相关硕士学位论文 前1条
1 孙亚兰;基于改进互信息的多尺度弹性配准方法研究[D];湘潭大学;2008年
，本文编号：2369459
本文链接：https://www.wllwen.com/guanlilunwen/gongchengguanli/2369459.html
