Investigation into lossless hyperspectral image compression for satellite remote sensing

Abstract

Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-infrared (IR), mid-IR and thermal IR portions of the spectrum, thus requiring large data storage on board the satellite and high bandwidth of the downlink transmission channel to ground stations. Image compression techniques are required to compensate for the limitations in terms of on-board storage and communication link bandwidth. In most remote-sensing applications, preservation of the original information is important and urges studies on lossless compression techniques for on-board implementation. This article first reviews hyperspectral spaceborne missions and compression techniques for hyperspectral images used on board satellites. The rest of the article investigates the suitability of the integer Karhunen–Loève transform (KLT) for lossless inter-band compression in spaceborne hyperspectral imaging payloads. Clustering and tiling strategies are employed to reduce the computational complexity of the algorithm. The integer KLT performance is evaluated through a comprehensive numerical experimentation using four airborne and four spaceborne hyperspectral datasets. In addition, an implementation of the integer KLT algorithm is ported to an embedded platform including a digital signal processor (DSP). The DSP performance results are reported and compared with the desktop implementation. The effects of clustering and tiling techniques on the compression ratio and latency are assessed for both desktop and the DSP implementation.