New-Tech Europe Magazine | Dec 2017

Imaging with Hyperspectral Sensors: The Optimum Design for your Application

Frederik Schönebeck, FRAMOUS

The relevant information in many vision applications is encoded into the color of the scenery. This information in normal color cameras is extracted based on the three standard color channels; red, green and blue (RGB), respectively. This color reproduction technique is an approximation and it is often insufficient to reliably solve a given machine vision problem. Hyperspectral imaging overcomes this limitation by providing a greater number of spectral bands, while maintaining an adequate spatial resolution. The required narrow-band spectral filters can be implemented at the sensor level owing to the recent advances in sensor design. Hyperspectral cameras equipped with these sensors show a significantly decreased level of complexity. This enables compact, reliable, and easy-to-use hyperspectral cameras that can benefit virtually any

application in which accurate color is key to success. Color is one of the key parameters in many vision applications and it is often used as a basis for classification, alternate background and foreground discrimination, or object identification. Typically, color cameras are equipped with three broadband color channels, red, green, and blue (RGB). These channels are implemented in the form of a regular, mosaic-like filter pattern; the so-called Bayer-pattern. With only these three standard filters, the resulting color information is merely approximate and often insufficient to reliably identify subtle color gradients. However, robust color discrimination is often the key to success (e.g., the discrimination between tissue, nerves and blood vessels during non-invasive surgery) and therefore the performance of conventional color sensors hampers

vision-based solutions in many applications. In contrast, hyperspectral imaging, or imaging spectroscopy, is a combination of digital imaging and narrow-band spectroscopy. This technique allows the light intensity to be captured for each pixel on the detector for a greater number of spectral bands (typically some tens to hundreds). Consequently, each pixel in the image contains a full color spectrum (in contrast to restricting three values for red, green and blue), which can characterize the scenery with greater color, detail, and accuracy. This feature enables object classification pipelines based on spectral properties, via statistical matching or neural networks; thereby enhancing entirely new approaches in the vision industry. The recent progress in sensor design and processing speed means that a wide field of applications now can

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