• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Agro-Geoinformatics >Research on a kind of remote sensing registration algorithm based on improved SIFT
【24h】

Research on a kind of remote sensing registration algorithm based on improved SIFT

机译：一种基于改进SIFT的遥感配准算法研究

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper offered an improved SIFT to deal the problems in Scale Invariant Feature Transform (SIFT) algorithm applied in remote sensing image registration, such as one's of massive data computation in the processing of registration, more feature categories is much with lower duplicate degree, complex feature extraction and matching process longer time-consuming, not matching the requirements of real-time, being sensitive to changes of gray value characteristics. The new SIFT algorithm speeds up the registration, making it more adapt to remote sensing image registration, stronger real-time performances. In this paper, the main issues are as follows: in the first place, in order to get rid of the noise and enhancing the effect of contour features, Fourier transform the pre-process remote sensing image date in remote sensing image, and time the result with high pass filtering. Secondly, establish differential Gaussian pyramid by DOG operator, and compute extreme points. And screen extreme points to reduce unnecessary points to deal with the sensitivity to gray value characteristics changes. We use the average value of 8 adjacent pixels to substitute the original extreme point in responsing to sensitivity of SIFT to the gray value characteristics changes. Using 128d vector to descript the feature points, so calculation by original algorithm is larger and lower. We prefer eight affine form concentric circles instead of 4 ∗ 4 board within the scope of 16 ∗ 16 pixels adjacent to the key points, making description of the key points reduced from the original 128 d to 64 d and improving the efficiency of the key points matching calculation. In order to pursue the best matching key points in the process of image registration, we use the two sets of remote sensing image's feature points get the pair set of optimized matching key points, then put the set in remote sensing image registration. Experiments with Gaofen-2 satellite remote sensing image data showing a higher computing speed and registration efficiency than the original algorithm.
机译：本文提出了一种改进的SIFT算法来解决尺度不变特征变换（SIFT）算法在遥感图像配准中的问题，例如在配准处理中要进行海量数据计算，特征类别多，重复度低，复杂等。特征提取和匹配过程耗时较长，不符合实时性要求，对灰度值特征的变化敏感。新的SIFT算法加快了配准速度，使其更适合于遥感图像配准，具有更强的实时性能。本文的主要问题如下：首先，为了消除噪声并增强轮廓特征的效果，傅里叶变换了遥感图像中的预处理遥感图像数据，并对图像进行时间校正。高通滤波的结果。其次，通过DOG算子建立微分高斯金字塔，并计算极限点。并且屏幕极端点减少了不必要的点，以应对对灰度值特性变化的敏感度。我们使用8个相邻像素的平均值代替原始极值，以响应SIFT对灰度值特征变化的敏感性。由于使用128d向量来描述特征点，因此使用原始算法进行计算的大小越来越大。在与关键点相邻的16 * 16像素范围内，我们首选8个仿射形式的同心圆而不是4 * 4板，从而使关键点的描述从原来的128 d减少到64 d，并提高了关键点的效率匹配计算。为了在图像配准过程中追求最佳的匹配关键点，我们利用两组遥感图像的特征点得到一对优化匹配的关键点集，然后将其放入遥感图像配准中。使用高分2号卫星遥感图像数据进行的实验显示出比原始算法更高的计算速度和配准效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号