Colour weight maps in visible and NIR image fusion

Duncan L. Hickman

doi:10.1117/12.2579221

8 November 2020 Colour weight maps in visible and NIR image fusion

Duncan L. Hickman

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115251O (2020) https://doi.org/10.1117/12.2579221
Event: SPIE Future Sensing Technologies, 2020, Online Only

Abstract

With the emergence of dual band colour (RGB) and Near Infrared (NIR) cameras, the attributes of both spectral regions can be combined through an image fusion process to enhance the imagery presented to an operator. In this paper, techniques for pixel-level image fusion of RGB and NIR (RGBN) are investigated. An RGBN fusion scheme is presented that employs a Colour Weight Map (CWM) to control colour distortion and enhance the visibility of scene features of interest. The CWM uses a Vegetation Index (VI) which is widely used by the remote sensing community for the analysis of crop health and other land resource applications. The RGBN fusion design is a simple arithmetic solution that is modified version of the Hue Saturation Intensity (HSI) and Brovey Transforms. Specifically, the CWM is used to adjust the NIR image prior to image fusion to reduce colour distortion effects. The fusion scheme can be readily implemented in hardware and can operate at high camera frame rates. Performance of the processing scheme is evaluated both qualitatively and quantitatively and a comparison with a more complex image pyramid fusion approach is also made. Several image metrics are used to examine both the colour distortions and retention of detail across a range of different image sets. The results from visual inspection and metric analysis are discussed and shown to be well correlated which is particularly important for those fusion schemes where the imagery is displayed directly to an observer.

Conference Presentation

Citation Download Citation

Duncan L. Hickman "Colour weight maps in visible and NIR image fusion", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115251O (8 November 2020); https://doi.org/10.1117/12.2579221

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