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Long-Wave Infrared and Visible Image Fusion for Situational Awareness. Nathaniel Walker. What is image fusion? Applications System-level considerations Image fusion algorithms Image quality metrics Further research. Agenda. Combine data from multiple sensors into a single image Visible

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Long-Wave Infrared and Visible Image Fusion for Situational Awareness

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Long wave infrared and visible image fusion for situational awareness l.jpg

Long-Wave Infrared and Visible Image Fusion for Situational Awareness

Nathaniel Walker


Agenda l.jpg

What is image fusion?

Applications

System-level considerations

Image fusion algorithms

Image quality metrics

Further research

Agenda


What is image fusion l.jpg

Combine data from multiple sensors into a single image

Visible

Image Intensified (I2)

Near Infrared (NIR)

Short-wave Infrared (SWIR)

Medium-wave Infrared (MWIR)

Long-wave Infrared (LWIR)

X-Ray

Enhance the capabilities of the human visual system

‘See’ outside the visible spectrum

All-weather visibility

What is Image Fusion?


Applications l.jpg

Surveillance and Targeting

Navigation

Applications

  • Satellites

  • Guidance/Detection Systems


System level considerations l.jpg

Parallax

Optical alignment

Image registration

Sensor pixel resolution

Color vs. grayscale

Spectral resolution can be lost in fusion

Human factors

Presentation of IR data

Realism of displayed data (superposition, contrast reversal)

Preserving relative intensity across the scene

System-Level Considerations


Image fusion algorithms zhang blum 1999 l.jpg

Weight-based combinations of the two sources

linear combination

general loss of contrast

Feature extraction

High-pass filtering or edge detection

Maximizing image quality metrics

Image Fusion Algorithms (Zhang, Blum 1999)


Image quality metrics l.jpg

Mostly done by subjective evaluation

‘Optimal’ methods are task and application dependent

Two classes of quantitative metrics (Chen, et al. 2005)

Analysis of the fused image

standard deviation – measure of contrast

entropy - measure of information content

SNR

Comparison of the fused image to the source images

cross-entropy

objective edge based measure

universal index based measure

Image Quality Metrics


Further research l.jpg

Concentration on grayscale fusion algorithms for effective communication of spectral information to the viewer

Sensor Assumptions

perfect optical alignment and image registration

same pixel resolution and field of view (FOV)

Compare quantitative metrics of image quality to subjective image evaluation for situational awareness

Focus on human factors for injecting infrared content into a visible spectrum image

What approach adds value without causing distraction or removing detail

Further Research


References l.jpg

References


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