Deep Learning for In-Orbit Cloud Segmentation and Classification in
  Hyperspectral Satellite Data
Daniel Kovac Jan Mucha Jon Alvarez Justo Jiri Mekyska Zoltan Galaz Krystof Novotny Radoslav Pitonak Jan Knezik Jonas Herec Tor Arne Johansen
This article explores the latest Convolutional Neural Networks (CNNs) for
cloud detection aboard hyperspectral satellites. The performance of the latest
1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and
2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed.
Evaluation criteria include precision and computational efficiency for in-orbit
deployment. Experiments utilize NASA's EO-1 Hyperion data, with varying
spectral channel numbers after Principal Component Analysis. Results indicate
that 1D-Justo-LiuNet achieves the highest accuracy, outperforming 2D CNNs,
while maintaining compactness with larger spectral channel sets, albeit with
increased inference times. However, the performance of 1D CNN degrades with
significant channel reduction. In this context, the 2D-Justo-UNet-Simple offers
the best balance for in-orbit deployment, considering precision, memory, and
time costs. While nnU-net is suitable for on-ground processing, deployment of
lightweight 1D-Justo-LiuNet is recommended for high-precision applications.
Alternatively, lightweight 2D-Justo-UNet-Simple is recommended for balanced
costs between timing and precision in orbit.