Lossless Predictive Compression of Hyperspectral Images

After almost three decades of successful data acquisition using multispectral sensors the first space based hyperspectral sensors were launched in 2000 on the NASA EO-1 satellite. However, airborne hyperspectral sensors such as AVIRIS, among others, have been generating useful data for many years. The advent of the space-borne ALI and Hyperion sensors as well as the successes of AVIRIS presage the development of many more hyperspectral instruments. Furthermore the success of multispectral imagers such as the Enhanced Thematic Mapper Plus (EMT+) on the LANDSAT-7 mission and the modestly named 36 band MODIS (Moderate Resolution Imaging Spectroradiometer) instrument aboard the Terra satellite and the Aqua spacecraft promises the deployment of significant numbers of other such instruments. The use of multispectral and hyperspectral sensors, while opening the door to multiple applications in climate observation, environmental monitoring, and resource mapping, among others, also means the generation of huge amounts of data that needs to be accommodated by transmission and distribution facilities that cannot economically handle this level of data. This means that compression, always a pressing concern [1], is now imperative. While in many cases the use of lossy compression may be unavoidable, it is important that the design always include the possibility of lossless recovery. Much effort usually has gone into the reduction of noise in the instruments. The voluntary addition of noise due to compression can be a bitter pill to swallow.