Hyperspectral remote sensing reveals detailed information about the optical response of a scene. Self-Organizing Maps (SOMs) can partition a hyperspectral dataset into clusters, both to enable more analysis on-board the imaging platform and to reduce downlink time. Here, the expected on-board performance of the SOM algorithm is calculated within two different satellite operational procedures: one in which the SOM is trained prior to imaging, and another in which the training is part of the operations. The two procedures are found to have advantages that are suitable to quite different situations. The computational requirements for SOMs of different sizes are benchmarked on the target hardware for the HYPSO-1 mission, and dimensionality reduction (DR) is tested as a way of reducing the SOM network size. We find that SOMs can run on the target on-board processing hardware, can be trained reasonably well using less than 0.1% of the total pixels in a scene, are accelerated by DR, and can achieve a relative quantization error of about 1% on scenes acquired by a previous hyperspectral imaging satellite, HICO. Moreover, if class labels are assigned to the nodes of the SOM, these networks can classify with a comparable accuracy to support vector machines, a common benchmark, on a few simple scenes.

This work describes the onboard image processing pipeline that is designed to be used onboard a CubeSat with a Hyper Spectral (Imager/Image) (HSI), namely the HYPSO-1 satellite. While the system itself supports both the use of a Field Programmable Gate Array (FPGA) and CPUs for processing, this work focuses on the latter. A reference to the source code is also enclosed within the text. Some of the planned and developed modules are presented, along with the design choices needed to accommodate the computationally constrained system. The execution time of the pipeline on target hardware with representative hyperspectral data is also shown. The results indicate that the modular pipeline framework is suitable for deployment onboard HYPSO-1.