A Novel Data Reutilization Strategy for Real-Time Hyperspectral Image Compression

The lossy compressor algorithm for hyperspectral image systems (HyperLCA) compressor is a transform-based algorithm specifically designed for the real-time compression of hyperspectral images captured by pushbroom scanners, using limited computational resources. It is based on the HyperLCA transform, which follows an unmixinglike strategy to independently compress each hyperspectral frame causally. A novel approach with respect to the original HyperLCA transform is introduced in this work. By reusing the information used to compress one frame in the subsequent frames, it has been possible to increase the HyperLCA transform compression performance and reduce its computational burden. Additionally, the proposed approach is applicable not only to the targeted compressor, but also to other causal hyperspectral analysis algorithms based on orthogonal projections and/or unmixinglike strategies. The proposed solution has been tested in a real unmanned aerial vehicle (UAV)-based acquisition platform, demonstrating the ability of our proposal to compress and transmit the captured hyperspectral data to a ground station in real-time.