HPC for SAR-MSS Data Fusion Experiments

Satellite image fusion techniques are compute intensive models mainly due to the availability of voluminous temporal data from multiple sensors. The need and support of High Performance Computing clusters in modelling various satellite data fusion techniques is unavoidable as the 5V’s such as volume, velocity, variety, value and veracity of space based Big Data has to be scientifically integrated over a HPC environment. Any such integration is meaningful, only when a suitable domain specific parallel processing algorithm is developed that can extract the much required critical information in near real time or real time mode. In this regard, this article mainly focuses on defining a HPC system environment and corresponding parallel algorithm which together supports the image fusion experiments of various space borne satellite data sets. Emphasis has been given in fusing different frequency, polarization Synthetic Aperture Radar’s, intensity data with Multi Spectral optical satellite data. The well proved and most commonly followed IHS and PAC image fusion techniques are studied and its parallel version of algorithm that can be enabled over the proposed HPC environment are discussed. In the current world scenario, setting up the proposed HPC system and enablement of proposed parallel models are unavoidable, mainly to address the real time requirements of defense and disaster management operations.