# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 1 Introduction  
Remote sensing satellites are an indispensable tool for Earth mapping and monitoring, and for improving the predictability of Earth system processes. Future remote sensing satellites that provide hyperspectral imagery promise to be a valuable source of information and enable new applications [1, 2]. There have been about two dozen spaceborne hyperspectral imaging sensors [3], starting with the Earth Observing-1 (EO-1) and Project for On-Board Autonomy 1 (PROBA-1) missions in the early 2000s [4, 5]. Further hyperspectral earth observation satellites continued to demonstrate the usefulness of hyperspectral data and many more are planned [6, 7, 8, 9, 10, 11, 12, 13]. However, the infrequent nature of their observations limits the use of hyperspectral sensors in applications with strong temporal requirements, such as disaster monitoring or the study of continuous ecosystem dynamics. Satellites with multispectral remote sensing instruments, such as those in the Landsat [14] and Sentinel [15] missions that record and downlink data continuously on the day side of the earth and rapidly provide data to end-users, have often been used for such applications. These satellites are able to provide rapid and temporally well-resolved data because, for a given area, they produce fewer data than hyperspectral imagers at a given spatial resolution. The challenges that hyperspectral missions face are exemplified by the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) mission [16], which, unlike the previous hyperspectral remote sensing missions, is envisioned to observe continuously, as opposed to observing a handful regions of interest per orbit,with a wider swath compared to the existing hyperspectral imagers. The envisioned continuous data acquisition rate of CHIME pushes the technical requirements of the mission. CHIME is proposed to feature a data downlink speed of 3.7 Gbit/s, which is more than an order of magnitude larger than the multispectral Sentinel-2 at 160 Mbit/s despite CHIME's smaller swath width. Because the CHIME hyperspectral instrument is proposed to gather data at excess of 5 Gbit/s, on-board cloud detection is necessary to fit the data within the downlink bandwidth [17]. On-board processing can be a solution for overcoming the downlink bottleneck for hyperspectral imaging satellites. Although it lacks both a hyperspectral imager and a specific scientific remote sensing mission, the OPS-SAT satellite is a demonstration of the potential for on-board processing in general [18], including image processing and machine learning models trained on-board the satellite after launch. The algorithms tested on OPS-Sat could in principle be adapted to run on larger satellite systems, such as CHIME. However, small satellites are often constrained by available processing power and energy [19; 20; 21]. Two recently launched cube satellites containing the HyperScout hyperspectral imager emphasize on-board processing capabilities. These are HyperScout on the GomX-4B satellite from 2018 [22; 23] and HyperScout-2 on the FSSCat B/\({}^{3}\)Cat 5B/\(\Phi\)-Sat-1 satellite from 2020 [24]. They were designed to perform standard processing steps like georeferencing and radiometric calibrations on-board. In addition, HyperScout-2's ability for on-board cloud segmentation was demonstrated on the ground; however, there are no published results yet about the on-board data processing capabilities. The CHIME mission proposes several on-board processing techniques for various applications and has investigated two specific methods in depth by testing on simulated data [17]. The first is cloud detection integrated with adaptive compression methods based on CCSDS123 [25], adapting to the cloud content in an image. The second method is image segmentation based on Support Vector Machines (SVMs). The authors of [26] briefly mention an issue with machine learning methods for hyperspectral remote sensing satellites, which is insufficient training data availability due to new sensors for each new mission.