基于快速算法的多传感器遥感图像对准

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tao Shu
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引用次数: 0

摘要

遥感影像技术对地面灾害评估和应急救援部署具有重要的指导意义。为了实现多传感器遥感图像的快速自动配准,引入了遥感图像分块配准思想,采用共轭梯度下降(CGD)方法对图像进行重构。结合函数拟合方法对尺度不变特征变换(SIFT)算法进行了改进和优化。这样可以提高多传感器遥感图像的配准精度和配准效率。结果表明,经CGD方法处理后的图像平均峰值信噪比为25.428。均方根平均值为17.442。平均图像处理时间为6.093 s。这些指标优于无源滤波算法和梯度下降法。改进SIFT配准方法的平均配准精度为96.37%,平均配准时间为2.14 s。这些指标明显优于传统的SIFT算法和加速鲁棒特征算法。实验证明,改进后的SIFT配准方法能有效提高多传感器遥感图像配准方法的精度和运算效率。改进的SIFT配准方法有效地解决了传统多传感器遥感图像快速配准方法精度低、耗时长的问题。在保持高配准精度的同时,提高了图像配准速度,为地震、洪水等重大灾害后的快速灾害评估提供技术支持。对开展高效的灾后救援部署具有重要价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-sensor remote sensing image alignment based on fast algorithms
Abstract Remote sensing image technology to the ground has important guiding significance in disaster assessment and emergency rescue deployment. In order to realize the fast automatic registration of multi-sensor remote sensing images, the remote sensing image block registration idea is introduced, and the image reconstruction is processed by using the conjugate gradient descent (CGD) method. The scale-invariant feature transformation (SIFT) algorithm is improved and optimized by combining the function-fitting method. By this way, it can improve the registration accuracy and efficiency of multi-sensor remote sensing images. The results show that the average peak signal-to-noise ratio of the image processed by the CGD method is 25.428. The average root mean square value is 17.442. The average image processing time is 6.093 s. These indicators are better than the passive filter algorithm and the gradient descent method. The average accuracy of image registration of the improved SIFT registration method is 96.37%, and the average image registration time is 2.14 s. These indicators are significantly better than the traditional SIFT algorithm and speeded-up robust features algorithm. It is proved that the improved SIFT registration method can effectively improve the accuracy and operation efficiency of multi-sensor remote sensing image registration methods. The improved SIFT registration method effectively solves the problems of low accuracy and long time consumption of traditional multi-sensor remote sensing image fast registration methods. While maintaining high registration accuracy, it improves the image registration speed and provides technical support for a rapid disaster assessment after major disasters such as earthquakes and floods. And it has an important value for the development of the efficient post-disaster rescue deployment.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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