An Embedded GPU Accelerated Hyperspectral Video Classification System in Real-Time

Uyperspectral imaging (HSI) has been adopted during the last years in different applications where material classification plays an important role. This favoured the improvement and development of new HS sensors, leading to HS snapshot cameras, where the sensor is able to acquire images in a realtime video fashion, reducing the spatial and spectral resolution for these cameras. However, although these cameras are able to acquire RAW HS images at high frames per second (FPS) rates, it is necessary to pre-process them to obtain an actual HS image and then obtain a classification map. This work addresses the development of an embedded CPU+GPU HS video classification system able to acquire and classify HS images in real-time video, i.e., at more than 25 FPS. Also, it includes a comparison where three different CPU+GPU embedded platforms are tested: NVIDIA Jetson Nano, NVIDIA Jetson TX1 and NVIDIA Jetson TX2. The results obtained show the feasibility of a HS video classification system using these embedded platforms.