A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of...A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.展开更多
靶标中心定位是红外热成像仪标定过程中的关键技术。针对靶标形貌相对复杂的特点,提出了一种基于自构卷积核改进模板匹配的中心定位算法。首先算法通过构造具有靶标图像特征的归一化模板,在下采样与预处理的目标图像上移动模板进行匹配...靶标中心定位是红外热成像仪标定过程中的关键技术。针对靶标形貌相对复杂的特点,提出了一种基于自构卷积核改进模板匹配的中心定位算法。首先算法通过构造具有靶标图像特征的归一化模板,在下采样与预处理的目标图像上移动模板进行匹配运算,得到粗定位结果。然后,根据粗定位中心对原图进行兴趣域(Region of Interest,ROI)精细匹配,并通过亚像素细分算法进一步校正。最终,确定准确的靶标中心位置。利用该算法对模拟的劣化环境下靶标图像进行检测,能有效避免模糊、背景复杂、目标不完整或者特征不明显的情况对定位的干扰,具有较好的鲁棒性,能够准确地定位靶标中心,且运算速度快,与互相关(Cross-Correlation,CCORR)、归一化互相关(Normalized Cross-Correlation,NCC)等传统模板匹配和Hough变换相比有较大的提升,可以满足红外热成像仪自动标定过程中的定位需求。展开更多
文摘A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.
文摘靶标中心定位是红外热成像仪标定过程中的关键技术。针对靶标形貌相对复杂的特点,提出了一种基于自构卷积核改进模板匹配的中心定位算法。首先算法通过构造具有靶标图像特征的归一化模板,在下采样与预处理的目标图像上移动模板进行匹配运算,得到粗定位结果。然后,根据粗定位中心对原图进行兴趣域(Region of Interest,ROI)精细匹配,并通过亚像素细分算法进一步校正。最终,确定准确的靶标中心位置。利用该算法对模拟的劣化环境下靶标图像进行检测,能有效避免模糊、背景复杂、目标不完整或者特征不明显的情况对定位的干扰,具有较好的鲁棒性,能够准确地定位靶标中心,且运算速度快,与互相关(Cross-Correlation,CCORR)、归一化互相关(Normalized Cross-Correlation,NCC)等传统模板匹配和Hough变换相比有较大的提升,可以满足红外热成像仪自动标定过程中的定位需求。
