Presentation + Paper
21 October 2016 Improved colour matching technique for fused nighttime imagery with daytime colours
Author Affiliations +
Abstract
Previously, we presented a method for applying daytime colours to fused nighttime (e.g., intensified and LWIR) imagery (Toet and Hogervorst, Opt.Eng. 51(1), 2012). Our colour mapping not only imparts a natural daylight appearance to multiband nighttime images but also enhances the contrast and visibility of otherwise obscured details. As a result, this colourizing method leads to increased ease of interpretation, better discrimination and identification of materials, faster reaction times and ultimately improved situational awareness (Toet e.a., Opt.Eng.53(4), 2014). A crucial step in this colouring process is the choice of a suitable colour mapping scheme. When daytime colour images and multiband sensor images of the same scene are available the colour mapping can be derived from matching image samples (i.e., by relating colour values to sensor signal intensities). When no exact matching reference images are available the colour transformation can be derived from the first-order statistical properties of the reference image and the multiband sensor image (Toet, Info. Fus. 4(3), 2003). In the current study we investigated new colour fusion schemes that combine the advantages of the both methods, using the correspondence between multiband sensor values and daytime colours (1st method) in a smooth transformation (2nd method). We designed and evaluated three new fusion schemes that focus on: i) a closer match with the daytime luminances, ii) improved saliency of hot targets and iii) improved discriminability of materials
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maarten A. Hogervorst and Alexander Toet "Improved colour matching technique for fused nighttime imagery with daytime colours", Proc. SPIE 9987, Electro-Optical and Infrared Systems: Technology and Applications XIII, 99870J (21 October 2016); https://doi.org/10.1117/12.2241627
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KEYWORDS
Sensors

Image fusion

Statistical methods

Image sensors

Environmental sensing

Visualization

Target detection

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