A novel data re-utilization strategy for real-time hyperspectral image compression

Abstract

The 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 unmixing-like strategy to independently compress each hyperspectral frame in a causal manner. 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 to 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 unmixing like strategies. The proposed solution has been tested in a real 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.