On-board compression of hyperspectral satellite data using band-reordering

Hyperspectral remote sensing has been widely utilized notably in high-resolution climate observation, environment monitoring, resource mapping. However, it brings undesirable difficulties for transmission and storage due to the huge amount of the data. The compression of the cube has been demonstrated to be an efficient strategy to solve these problems. Moreover, the data features have strong similarity in disjoint spectral regions due to the same type of absorbing gases. That is why a pre-processing scheme based on a similarity measurement and a reordering strategy permits to enhance the compression ratio. In this work, we first propose a review of similarity measurements and reordering strategies, and we give the field of application of each of them. In particular, we propose a pre-selection of these measurements and re-ordering strategies with respect to the expected performance, the complexity and the robustness to an on-board implementation. In a second part, we give the performance gap between a high performance / complex approach and a spatializing approach for two compression schemes: a 3D transform and a 3D predictive algorithm. Finally, we present the capability to implement the reordering in a semi-optimal, semi-fixed or fixed manner, and thereby characterize the performances in a space borne system.