A Modular Hyperspectral Image Processing Pipeline For Cubesats

Hyperspectral imaging is a powerful remote sensing technology, but its use in space is limited by the large volume of data it produces, which leads to a downlink bottleneck. Therefore, most payloads to date have been oriented towards demonstrating the scientific usefulness of hyperspectral data sporadically over diverse areas rather than detailed monitoring of spatio-spectral dynamics. The key to overcoming the data bandwidth limitation is to process the data on-board the satellite prior to downlink. In this article, the design, implementation, and in-flight demonstration of the on-board processing pipeline of the HYPSO-1 cube-satellite are presented. The pipeline provides not only flexible image processing but also reliability and resilience, characterized by robust booting and updating procedures. The processing time and compression rate of the simplest pipeline, which includes capturing, binning, and compressing the image, are analyzed in detail. Based on these analyses, the implications of the pipeline performance on HYPSO-1’s mission are discussed.

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“…Currently implemented stages and their motivation are shown in Table 4. These stages have been tested on the ground [71].…”

Section: Reconfigurable Pipeline Frameworkmentioning

confidence: 99%

Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite

et al. 2023

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“…The concept of operations is explained in the following: A given phenomenon on the Earth's surface could be identified by HYPSO-2. The satellite may then pre-process the Hyperspectral image by means of compression, classification [3] and georeferencing, as the amount of data for the SDR link would need to be kept low. The automation of operations for the cooperation of HYPSO-2 and ASV requires the definition of imposed constraints to ensure that, for example, the ASV is directed to the right area, or that the measurement taken onboard is reliable and relevant.…”

Section: Introductionmentioning

confidence: 99%

Onboard Hyperspectral Classification Enables Georeferencing

Chiatante,

Langer,

Garrett

et al. 2023

2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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The Norwegian University of Science and Technology (NTNU) has been building a system composed of multiple remote sensing agents for ocean observations. NTNU launched the first HYPerspectral Smallsat for Ocean observation (HYPSO-1) satellite on the 13 th of January 2022, adding a Hyperspectral Imager satellite to its disposal, while HYPSO-2 will be launched in 2024. However, due to the high dimensionality of the collected data, onboard processing has been introduced on the satellite to potentially deliver information in a more rapid and condensed manner. In this paper, the operational use of onboard classification will be explored, investigating how different algorithms can be applied for data reduction to potentially enable decision-making onboard the satellite such as the use of classified images for onboard georeferencing. The classification could be used to enable near real-time data latency cooperation between the satellite and the other agents, such as autonomous surface vehicles for in-situ measurements.

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