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Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing

机译：中空无人机遥感影像的可见光与红外一体化摄像机融合

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摘要

This study proposes a novel method for image registration and fusion via commonly used visible light and infrared integrated cameras mounted on medium-altitude unmanned aerial vehicles (UAVs).The innovation of image registration lies in three aspects. First, it reveals how complex perspective transformation can be converted to simple scale transformation and translation transformation between two sensor images under long-distance and parallel imaging conditions. Second, with the introduction of metadata, a scale calculation algorithm is designed according to spatial geometry, and a coarse translation estimation algorithm is presented based on coordinate transformation. Third, the problem of non-strictly aligned edges in precise translation estimation is solved via edge–distance field transformation. A searching algorithm based on particle swarm optimization is introduced to improve efficiency. Additionally, a new image fusion algorithm is designed based on a pulse coupled neural network and nonsubsampled contourlet transform to meet the special requirements of preserving color information, adding infrared brightness information, improving spatial resolution, and highlighting target areas for unmanned aerial vehicle (UAV) applications. A medium-altitude UAV is employed to collect datasets. The result is promising, especially in applications that involve other medium-altitude or high-altitude UAVs with similar system structures.
机译：这项研究提出了一种通过安装在中空无人机上的常用可见光和红外集成摄像机进行图像配准和融合的新方法。图像配准的创新在于三个方面。首先，它揭示了在远距离和平行成像条件下，如何将复杂的透视转换转换为两个传感器图像之间的简单比例转换和平移转换。其次，随着元数据的引入，根据空间几何设计了尺度计算算法，并提出了基于坐标变换的粗略平移估计算法。第三，通过边缘距离场变换解决了精确平移估计中非严格对齐边缘的问题。为了提高效率，提出了一种基于粒子群算法的搜索算法。此外，基于脉冲耦合神经网络和非下采样轮廓波变换设计了一种新的图像融合算法，以满足保留颜色信息，添加红外亮度信息，提高空间分辨率以及突出无人机的目标区域的特殊要求。应用程序。采用中空无人机来收集数据集。结果是有希望的，特别是在涉及具有类似系统结构的其他中海拔或高海拔无人机的应用中。

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