Low complexity distributed approach to Hyperspectral image compression

Application of Hyperspectral imagery has accomplished prevalence in the field of remote sensing, hence has emanated as a very progressive field for research and development for the past couple of decades. The sententious spatial and spectral correlation divulged by the Hyperspectral images is oppressed for compression. In this paper, we affirm a near lossless compression of Hyperspectral images based on distributed source coding. Slepian-Wolf theory forms the groundwork for the exertion of the distributed source coding principle. This compression methodology is enforced on to the collimated blocks of similar size. As the information content alters from block to block, a rate is attributed to each block under the attainable rate coercion. The blocks are quantized and encoded using the Slepian-Wolf coding. The side information requisite for the Slepian-Wolf encoding is forged using linear prediction model. This compression technique aims to achieve high compression performance with low complexity and is compared with the extant compression algorithms.