HyperField, a constellation of polar sun-synchronous nanosatellites, is being developed by the
Finnish company Kuva Space. The constellation that will be launched in 2023 consists of 100
CubeSats with hyperspectral systems operating in the visible to near-infrared (VIS-NIR,
450-1100 nm) or Visible-to-shortwave infrared (VIS-SWIR, 450-2500 nm) ranges and provides
two to three times daily images of every location on Earth.
This paper presents the first and second generations of the Hyperfield satellites. It reviews along with their innovative platform and detector technology, the optical modes, planned mission operations, advanced AI-based processing architecture and novel algorithms are developed to improve the acquisition, enhance the image quality and produce tailored-based services.
Search and Rescue (SAR) operations following natural or anthropogenic disasters are often hindered by smoke, rain, fog and haze. Developing methods that combat Degraded Visual Environments (DVE) and ensure the rapid detection of survivors and rescue personnel after a disaster is crucial to reducing mortality.
This paper proposes a novel AI scheme to detect static and moving objects using snapshot NIR hyperspectral data. The proposed model leverages spatial features through object detection to identify objects and, at the same time, applies a semantic segmentation algorithm based on spectral features to validate the presence of firefighters within the detected bounding boxes. The training is optimised for real-time high-performance inference by exporting it to TensorRT. This approach has been successfully demonstrated in various realistic scenarios with an F1-score of 0.923 and 77.9 frames per second.
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