Big data processing using hpc for remote sensing disaster data
Voluminous data (Multispectral, Hyperspectral) from Variety of sensors (Airborne sensors, space borne sensors) with Velocity (high temporal resolution) when used for decision making to support natural disasters such as earthquakes, floods, oil-spills etc., for near real time accurate responses, is a problem that needs Big Data Analytics. To gain rapid insight from this big data, high performance computing (HPC) with some scalable solution that reduces the execution time are in extreme demand. To serve this real time need, scalable hybrid parallelism approach based on state of art multi-core GPUs and Message Passing Interface (MPI) is explored for analyzing remote sensing disaster data. Spatio-temporal remote sensing data of oil-spill at Gulf of Mexico captured by LANDSAT 7 ETM+ is considered for analysis. The core objective includes performance evaluation of the analysis process across various parallel implementation platforms.