Deep Learning for Segmentation of Hyperspectral Satellite Images

This project trained convolutional neural networks for semantic segmentation of hyperspectral satellite imagery from the HYPSO-2 mission, classifying each pixel as sea, land, or cloud.

The pipeline addresses the challenge of training on imagery with hundreds of spectral bands per pixel, orders of magnitude richer than RGB but far more compute-intensive. NVIDIA GPU cluster acceleration was used to optimize training speed and enable rapid model iteration.