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Multi-Sensor Data Fusion across Time and Space

机译：跨时空的多传感器数据融合

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Field measurement campaigns typically deploy numerous sensors having different sampling characteristics for spatial, temporal, and spectral domains. Data analysis and exploitation is made more difficult and time consuming as the sample data grids between sensors do not align. This report summarizes our recent effort to demonstrate feasibility of a processing chain capable of "fusing" image data from multiple independent and asynchronous sensors into a form amenable to analysis and exploitation using commercially-available tools. Two important technical issues were addressed in this work: 1) Image spatial registration onto a common pixel grid, 2) Image temporal interpolation onto a common time base. The first step leverages existing image matching and registration algorithms. The second step relies upon a new and innovative use of optical flow algorithms to perform accurate temporal upsampling of slower frame rate imagery. Optical flow field vectors were first derived from high-frame rate, high-resolution imagery, and then finally used as a basis for temporal upsampling of the slower frame rate sensor's imagery. Optical flow field values are computed using a multi-scale image pyramid, thus allowing for more extreme object motion. This involves preprocessing imagery to varying resolution scales and initializing new vector flow estimates using that from the previous coarser-resolution image. Overall performance of this processing chain is demonstrated using sample data involving complex too motion observed by multiple sensors mounted to the same base. Multiple sensors were included, including a high-speed visible camera, up to a coarser resolution LWIR camera.
机译：现场测量活动通常针对空间，时间和光谱域部署大量具有不同采样特性的传感器。由于传感器之间的样本数据网格未对齐，因此数据分析和开发变得更加困难且耗时。该报告总结了我们最近的努力，以证明能够将来自多个独立和异步传感器的图像数据“融合”为适合使用商用工具进行分析和开发的形式的处理链的可行性。这项工作解决了两个重要的技术问题：1）将图像空间配准到一个公共像素网格上，2）将图像时间内插到一个共同的时基上。第一步利用现有的图像匹配和配准算法。第二步依赖于光流算法的创新应用，以对较慢的帧频图像执行准确的时间上采样。光流场矢量首先从高帧频，高分辨率图像中得出，然后最终用作对较慢帧频传感器的图像进行时间上采样的基础。使用多尺度图像金字塔计算光流场值，从而允许更极端的物体运动。这涉及对图像进行预处理以改变分辨率比例，并使用先前的较高分辨率图像的图像矢量估计值进行初始化。使用包含安装在同一基座上的多个传感器观察到的涉及复杂太运动的样本数据来演示此处理链的总体性能。包括多个传感器，包括高速可见摄像机，以及分辨率更高的LWIR摄像机。

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