HYPERSPECTRAL IMAGING FEATURE


Her team have so far been working in a


controlled environment to classify plastics. Different polymers will have different chemical fingerprints when viewed through a SWIR hyperspectral camera, meaning the technology can differentiate between material that would be indistinguishable under visible light. One benefit of hyperspectral imaging is that it combines both spectral and spatial information. It then requires advanced image analytics including classification and segmentation algorithms to make sense of the data, which is what the Texas A&M team are working on. The researchers are using a SWIR spectral


imager from Surface Optics. Most of Surface Optics’ work is for the military; it’s been providing SWIR video-rate spectral imagers for the military for almost 10 years. ‘It’s really valuable for identifying energetic materials or any material that would indicate explosives,’ explained Austin Van Sickle, the firm’s technical sales lead. ‘The imagers can do this for trace amounts outside, in “the theatre”, as it’s called in the military,’ he continued. ‘This is the same process as looking for plastics in the environment.’ Van Sickle said that a hyperspectral camera can easily be attached to a drone to survey a river looking for plastics. However, most cameras tend to be scanning systems, which means they have to be looking straight down and scan across a stretch of land in a very ordered way. There’s nothing wrong with doing this, but it is time consuming. The


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‘The next steps are improving identification and segmentation algorithms to properly label plastics’


advantage with using Surface Optics’ spectral cameras, according to Van Sickle, is that they can pan around the environment. ‘For the military, nothing is ordered; they


want real-time data,’ he said. Surface Optics provides a staring system, which is a video- rate imager operating at 30fps that can pan around similar to a video camera. Professor Mehrubeoglu and her team are


now trying to fine-tune the classification algorithms for robust segmentation, so that the hyperspectral data can be interpreted reliably.


One of the challenges with using SWIR


hyperspectral imaging to seek plastic in rivers is that water is opaque under SWIR light. Nevertheless, there are approaches to finding plastics in and around water using data captured in SWIR wavelengths, Mehrubeoglu said. ‘Plastics on land, near the shore or floating on the water surface, are much easier to detect and identify than those that are small and may be submerged in water,’ she said. Another challenge is that plastics


deteriorate over time, and their physical attributes and spectral signatures also change, Mehrubeoglu continued. ‘Accuracy in plastics characterisation is a challenge when there


Waste sorting


Spotting plastic pollution in the environment is one thing, but hyperspectral imaging also promises to improve plastic sorting in recycling plants. ‘There are a lot of research groups working on feasibility studies for sorting plastics with hyperspectral imaging. Now companies are picking this up and starting to do the application research,’ explained Wouter Charle, hyperspectral imaging expert at Belgian R&D institute Imec. Imec provides two types of hyperspectral


camera: its Snapscan technology has a line scan sensor that is scanned on an internal


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exists plastic debris of varying state, shape, texture, colour and size. Improving accuracy is an ongoing research goal, particularly on the post-data acquisition side,’ she said. Mehrubeoglu added that real-time


applications for environmental surveying will need better coupling of hardware and software. ‘By finding effective bands that increase contrast among a variety of plastics in the scene, image processing techniques can also be applied and combined with spectral analysis to perform semantic image segmentation and image mapping of plastics,’ she said. ‘Currently, hardware exists to collect useful SWIR hyperspectral data in outdoor settings using natural light. The next steps are improving identification and segmentation algorithms to properly label plastics based on their class or type. Machine learning will be a promising solution for this application.’


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