The HYPSO-1 satellite, a 6U CubeSat carrying a hyperspectral imager, was launched on 13 January 2022, with the Goal of imaging ocean color in support of marine research. This article describes the development and current status of the mission and payload operations, including examples of agile planning, captures with low revisit time and time series acquired during a campaign. The in-orbit performance of the hyperspectral instrument is also characterized. The usable spectral range of the instrument is in the range of 430 nm to 800 nm over 120 bands after binning during nominal captures. The spatial resolvability is found empirically to be below 2.2 pixels in terms of Full-Width at Half-Maximum (FWHM) at 565 nm. This measure corresponds to an inherent ground resolvable resolution of 142 m across-track for close to nadir capture. In the across-track direction, there are 1216 pixels available, which gives a swath width of 70 km. However, the 684 center pixels are used for nominal captures. With the nominal pixels used in the across-track direction, the nadir swath-width is 40 km. The spectral resolution in terms of FWHM is estimated to be close to 5 nm at the center wavelength of 600 nm, and the Signal-to-Noise Ratio (SNR) is evaluated to be greater than 300 at 450 nm to 500 nm for Top-of-Atmosphere (ToA) signals. Examples of images from the first months of operations are also shown.

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.

AI on spaceborne platforms optimizes operations and increases automation, crucial for satellites with limited downlink capacity. It can ensure that only valuable information is transmitted, minimizing resources spent on unnecessary data, which is especially important in hyperspectral Earth Observation missions, producing large data volumes. Our previous work showed that the 1D-CNN, 1D-Justo-LiuNet, outperformed 2D-CNNs and Vision Transformers for hyperspectral segmentation with an accuracy of 0.93 and 4563 parameters, making our model the best choice for in-orbit deployment. While the state of the art has deployed 1D-CNNs on low-power platforms, such as Unmanned Aerial Vehicles, they have still not been deployed in space before. In this work, we mark the first deployment and testing of a 1D-CNN in a satellite. We implement a C version of the 1D-Justo-LiuNet and, after ground validation, we deploy it on board the HYPSO-1 satellite. We demonstrate in-flight segmentation of hyperspectral images via the 1D-CNN to classify pixels into sea, land, and cloud categories. We show how in-orbit segmentation improves satellite operations, increases automation, and optimizes downlink. We give examples of how in-orbit segmentation addresses mission challenges in HYPSO-1, such as incomplete data reception, incorrect satellite pointing, and cloud cover, helping to decide whether to transmit or discard data on board. An additional CNN autonomously interprets the segmented images, enabling on-board decisions on data downlink.

Traditional large Earth Observation Satellites (EOS) are designed with simple imaging strategies. However, small satellites can satisfy immediate user needs through agile imaging modes. In this paper, we model several agile imaging mode concepts and test them experimentally on the push-broom hyperspectral imaging cubesat HYPSO-1. Six imaging modes are tested: slewing, multi-target, dual-angle, wide swath, on-board processing, and dynamic pointing. The results show that the slewing and multi-angle mode increased estimated Signal-to-Noise Ratio (SNR) by 1.4x to 1.7x, the on-board processing mode decreased data latency, and the multi-target, wide swath and dynamic pointing modes enhanced spatial coverage.

The large size of hyperspectral images and the computational demands required for interpreting them complicates their acquisition, distribution, and utilization by remote agents. The Norwegian University of Science and Technology is building an ocean observation system, including the HYPSO-1 hyperspectral imaging satellite, to monitor ocean color in coastal waters. The images collected by HYPSO-1 will be used to inform an ensemble of robotic agents which locally monitor ocean conditions. Several agents will be unmanned aerial vehicles that likewise carry hyperspectral imagers. Hyperspectral images will be processed on-board the different agents, as well as on the ground. Here, we discuss the architecture, development, current status, and future opportunities of hyperspectral image processing pipelines on these platforms.

This paper presents the software architecture, development, and integration of a Commercial Off-The-Shelf (COTS) based hyperspectral imaging payload onboard the HYPerspectral Smallsat for Ocean observation (HYPSO-1) CubeSat. The chosen service-oriented software architecture provides a modular design that is planned to aid future development. The image processing onboard HYPSO-1 will be updated in-flight. We discuss here the strengths and weaknesses of our development procedures for software. The issues reported during development were analyzed and categorized, and the findings from these issues indicate the importance of early testing, code reviews, and the continuous availability of target hardware for successful software integration when relying on a modular design. A perspective of the benefits of the software architecture for a CubeSat subsystem is also given.