Ye Zhen, Kang Jian, Yao Jing, Song Wenping, Liu Sicong, Luo Xin, Xu Yusheng, Tong Xiaohua
College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China.
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany.
Sensors (Basel). 2020 Aug 4;20(15):4338. doi: 10.3390/s20154338.
Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, -norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.
多传感器图像的自动精确配准在许多遥感应用中起着至关重要的作用。然而，由于显著的辐射和纹理差异，这始终是一项具有挑战性的任务。本文提出了一种增强的亚像素相位相关方法，该方法将基于相位一致性的结构表示、基于 -范数的带自适应掩膜的秩一矩阵逼近以及稳健的鲁棒模型拟合嵌入到频域中的传统计算框架中。目的是在实际情况下提高亚像素平移估计的准确性和鲁棒性。此外，集成了使用增强的亚像素相位相关的模板匹配以实现可靠的精确配准，其能够提取足够数量的分布良好且高精度的同名点，并减少粗配准的多传感器遥感图像的局部错位。使用来自不同卫星和传感器的图像进行的实验分为两部分：同名点匹配和精确配准。定性分析结果以及与当前基于区域和基于特征的匹配方法的定量比较证明了所提方法用于多传感器匹配和配准的有效性和可靠性。
