Novel 2D Hyperspectral Imaging, delivering a new realm of information for a variety of next generation applications

The majority of today’s space-based sensors, whether looking at other space assets for domain awareness, or for remote sensing of objects on the ground, have little persistence to temporally observe an object. Much of the associated concept of observation is due to the orbital speed of the spacecraft and revisit time. In this regard, sensors have been primarily focused on collecting as much volume of area per orbital pass as possible. This approach creates huge volumes of data that must be downlinked. However, the downlink bandwidth is limited and much of the data is inherently time-sensitive. To compensate for this bottleneck, sensors have been designed with only a few electromagnetic bands, which limits the data collected. In other words, priority is given to collecting a large area versus collecting more useful data per area. Looking back, one can see that sensor design has been relatively static since Landsat, for 40 years. Without the right data, many applications that might otherwise have great utility are not possible. In addition, the collection scheme (essentially line scanning) limits the flexibility of most collections to only occur within some small ideal angle. In this regard, satellites wait for this ideal angle to occur (which might take days), which is not useful for some time sensitive applications. This paper and session explores the concept of a 2D Hyperspectral Imaging (HSI) system that focuses on greater data per area. While historically that would mean limited area coverage, the advancement of inexpensive satellite development, much lower launch costs (as little as $1.5M per 500 lbs to low earth orbit), and new downlink architectures make it reasonably affordable to place a constellation in orbit that can provide the desired area coverage and get the data into the hands of users in a timely manner. The result will be the ability to deliver a new realm of information and applications.