New-Tech Europe Magazine | Dec 2017

in pushbroom-scanning hyperspectral cameras is on the order of one hundred. This feature yields very detailed spectral information, which in turn enables more reliable identification and classification results. At the same time, the maximum spatial resolution is very high, and is determined by the raw resolution of the sensor along one dimension (typically 2048 − 4096 pixels), and the scanning speed along the other dimension. It is important to note that the high spectral and spatial resolutions come at the expense of the scanning requirements, and can potentially lead to more complicated application setups. The scanning requirements, however, are often intrinsic to the application, and therefore this is not considered to be a general disadvantage. Snapshot Mosaic Snapshot mosaic hyperspectral cameras are very similar to standard color cameras. The filter coating is arranged as a mosaic of repetitive tiles, but, contrary to the 2 × 2 Bayer pattern, typically these tiles consist of 4 × 4 or 5 × 5 pixels. The individual pixels in each of these tiles are coated with narrowly-defined bandpass filters (compare with Figure 2) Therefore, the number of spectral bands is significantly increased compared to the traditional red, green, and blue color channels. It is important to note that this gain in spectral information is accompanied with a decrease in spatial resolution, which results from the large size of the individual tiles in the filter mosaic. Typically, the resulting raw resolutions are of the order of 500 × 250 pixels, but can be increased with sophisticated interpolation algorithms. The complete spatial and spectral information can be obtained in one snapshot, as implied by the name, and for this reason snapshot mosaic hyperspectral cameras can be used

for conventional video acquisition, or other applications where scanning is not applicable. Consequently, snapshot mosaic hyperspectral cameras are very versatile and can be easily integrated into virtually any application, as a substitute for conventional color cameras. These applications include quality inspection, food sorting, tissue analysis, endoscopy, and microscopy. The only drawback of this “ease-of- use” is the limited number of spectral bands of approximately 20 compared to over a 100 with pushbroom- scanning cameras. However, often this is still sufficient to address imaging problems that cannot be solved with normal color cameras. Further considerations Both the sensor designs explained above do not put any special constraints on employed lenses, other than a high transmission and low chromatic aberration over the spectral range of interest. Consequently, cameras with hyperspectral image sensors can be readily equipped with existing professional-grade machine vision lenses. The output of hyperspectral cameras comes in the form of 3D data cubes, with two spatial and one spectral dimension, i.e., a full spectrum for each pixel. The concept of this type of data cube is depicted in Figure 3, where x and y represent the well-known spatial dimensions of the image and the vertically arranged λ1..n represent the n spectral bands. Note that a significant amount of image post processing is required to transform the raw information into data that can be further implemented for object identification or classification. Conclusion It is now possible to implement narrow-band spectral filters at the pixel-level with semiconductor thin-film processing. Hyperspectral

Figure 3: A 3D data cube, with x and y representing spatial dimensions, and λ1..n depicting n spectral bands. Image credits: XIMEA

cameras using this technology can be implemented as reliable, compact, and easy-to-use systems that can be integrated into many different applications. These applications can range from precision agriculture supported by unmanned vehicles, to robust discrimination between tissue, nerves, and blood vessels during non-invasive surgery. In addition, this technology can also significantly improve food sorting or quality inspection, by providing more detailed and accurate spectral data than conventional color sensors. Specifically, when combined with powerful computing approaches like neural networks, capable of analyzing and extracting the desired information from vast amounts of raw data, hyperspectral cameras will enhance virtually all applications in which the color of the object plays a crucial role.

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