存在噪声时最先进的多模态遥感图像匹配方法的性能评估

IF 2.3 Q2 REMOTE SENSING
Negar Jovhari, Amin Sedaghat, Nazila Mohammadi, Nima Farhadi, Alireza Bahrami Mahtaj
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引用次数: 0

摘要

迄今为止,已有多种图像配准方法用于处理多模态图像对之间的失真问题。然而,由于不可避免地存在大量噪声,许多传统和先进的方法都大打折扣。关键在于选择一种高鲁棒性的局部特征检测和描述方法作为许多匹配框架的原则。然而,很少有研究集中处理噪声问题。为此,本文针对人工序列噪声水平,对最著名和最先进的特征描述器进行了评估。所采用的方法包括各种基于人工学习的描述符。本文还进一步指出,除了设计的结构特征图外,空间排列和支持区域大小等多重标准在实现成功匹配方面也发挥了作用,尤其是在多模态图像之间存在剧烈噪声和复杂失真的情况下。此外,为了滤除噪声特征,所采用的局部特征检测器与统一能力算法相结合。实验结果表明,由于设计了先进的集成内核和极性排列,MKD(多内核描述符)具有整体优势(平均 20.0%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Performance evaluation of state-of-the-art multimodal remote sensing image matching methods in the presence of noise

Performance evaluation of state-of-the-art multimodal remote sensing image matching methods in the presence of noise

To date, various image registration approaches have been conducted to deal with distortions between multimodal image pairs. However, significant existing noise as an unavoidable issue deteriorates many conventional and advanced methods. The critical key is to choose a highly robust local feature detection and description method as the principle for many matching frameworks. However, few studies have concentrated on dealing with the noise issue. For this purpose, this paper evaluates the most well-known and state-of-the-art feature descriptors against artificial sequential noise levels. The employed methods consist of various handcrafted learning-based descriptors. It is further indicated that in addition to the designed structural feature map, multiple criteria, such as spatial arrangement, and the magnitude of the support area, play roles in achieving successful matching, especially in the presence of dramatic noise and complex distortion between multimodal images. Moreover, to filter out the noisy features, the employed local feature detectors are integrated with the uniform competency algorithm. Experimental results demonstrate the overall superiority (20.0% on average) of the MKD (multiple-kernel descriptor) due to advanced designed integrated kernels and polar arrangements.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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