Multisensor data fusion across time and space

Pierre V. Villeneuve; Scott G. Beaven; Robert A. Reed

doi:10.1117/12.2050733

13 June 2014 Multisensor data fusion across time and space

Pierre V. Villeneuve, Scott G. Beaven, Robert A. Reed

Proceedings Volume 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX; 90880S (2014) https://doi.org/10.1117/12.2050733
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

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.

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Pierre V. Villeneuve, Scott G. Beaven, and Robert A. Reed "Multisensor data fusion across time and space", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 90880S (13 June 2014); https://doi.org/10.1117/12.2050733

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KEYWORDS

Sensors

Image sensors

Optical flow

Image processing

Image registration

Long wavelength infrared

RGB color model

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