Onboard Hyperspectral Image Compression Using Compressed Sensing and Deep Learning

We propose a real-time onboard compression scheme for hyperspectral datacube which consists of a very low complexity encoder and a deep learning based parallel decoder architecture for fast decompression. The encoder creates a set of coded snapshots from a given datacube using a measurement code matrix. The decoder decompresses the coded snapshots by using a sparse recovery algorithm. We solve this sparse recovery problem using a deep neural network for fast reconstruction. We present experimental results which demonstrate that our technique performs very well in terms of quality of reconstruction and in terms of computational requirements compared to other transform based techniques with some tradeoff in PSNR. The proposed technique also enables faster inference in compressed domain, suitable for on-board requirements.