# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 1 Introduction
Their solution was to emulate the new camera sensor characteristics and generate simulated data. However, the data volume issue could also be solved via updates to the on-board software with new machine learning algorithms at a later stage during the mission, or via online learning [27]. Few previous missions had software updates as a part of the concept of operations to extend on-board processing functionality. Software updates of an operational nature have been performed during the operation of the Aalto-1 Spectral Imager mission [28]; however, no updates of on-board processing capabilities have been reported. Although it is yet to be launched, the Intuition-1 mission will develop a small satellite, a 6U CubeSat, to be a testbed for various Artificial Intelligence (AI) algorithms for hyperspectral data processing [29]. The previously mentioned OPS-SAT has received many updates to its software in the years since its launch [18; 27]. Since cube-satellites do not require as large an investment as larger satellites, a few hyperspectral imaging cubesats motivated by specific applications have been launched. The GHG-sat constellation now consists of over 10 satellites that image with very fine spectral resolution within the short wave infrared portion of the spectrum in order to detect methane gas emissions [30]. While on-board real-time detection of methane has been demonstrated [31], the on-board payload processing is responsible only for downlinking data and telemetry and most of the data processing is still performed on the ground. The lack of on-board processing on GHG-sat, even though relevant algorithms have been demonstrated, suggests that additional research is needed to address concerns about its reliability or worth. In contrast, the Hyper-Spectral Imager for Oceanographic Observations (HYPSO) mission, which uses visible/near-infrared imagers with a transmission grating design for algal bloom monitoring, includes on-board processing as a critical part of its mission [32; 33; 34]. A special focus of the first satellite in the constellation, HYPSO-1 (NORAD ID: 51053), launched on 13 January 2022, lies in reducing data and information latency from observation to availability to the end-user [35] because algal blooms are sporadic, spontaneous, and evolve continually. Therefore, a re-configurable on-board processing unit is included as part of the mission. Not only can HYPSO-1 deliver standard hyperspectral data products, it is also a research infrastructure for testing new on-board data processing algorithms. Similar to PROBA-1 in 2001, and recent enMAP and PRISMA satellites, HYPSO-1 can point off-nadir to facilitate timely imaging of chosen targets [32; 36]. In this article, the on-board image processing pipeline of HYPSO-1 is presented. Various configurations of the pipeline achieve specific operational goals, which include the reduction of data volumes by lossless compression, mitigation of noise, extraction of features of interest, and computation of derived higher-level products [37]. The simplest of these variants, the Minimal On-board Image Processing Pipeline (MOBIP), has been executed over 1000 times in orbit. Fast processing and downlink enable early warnings for Harmful Algal Bloom (HAB) and the collaboration with other ocean remote sensing and in situ agents for real-time ocean monitoring. For example, if the spatial location of a possible algal bloom is imaged and identified by low latency on-board processing, that location information can be immediately downlinked and received by in situ agents, such as autonomous surface vehicles, which can travel to the location to investigate [35]. Because HYPSO-1 is driven by a specific, time-sensitive mission it shows clearly how an on-board image processing pipeline can be used to meet specific scientific and societal needs. The scientific contribution of this paper is the design of a robust hyperspectral remote sensing satellite payload system with reconfigurable on-board processing capabilities.