Recent Developments in High Performance GeoComputation for Massive Remote Sensing Data
Haiping Yang, Zhanfeng SEHN, Jiancheng Luo, Wei WU · Geo-information Science · 2013
As the amount of remote sensing data is sharply increasing within the continuing development in remote sensors,the exploitation of massive amount of remote sensing data is booming in recent years.Therefore,the computational problems in the applications that involve the large collection of remotely sensed imagery processing,such as global climate change and hazard assessment,arise inevitably.On this point,high performance computing(HPC)-based patterns,including cluster computing,grid computing,cloud computing and computing with hardware such as field-programmable gate arrays(FPGA) and graphic processing units(GPU),are introduced to the applications that concern a huge amount of remote sensing data processing.This paper focuses on the state of the art coping with the challenges that emerge when the massive remote sensing data are processed by the HPC-based platforms.In particular,we review recent developments in the parallel file systems for storing the remote sensing data and high performance geocomputation.Specifically,the HPC-based paradigms delivered in this paper involve cluster-based platform,grid and cloud based environments.Further,the typical examples of the HPC-based platforms that process the massive remote sensing data,comprising the Pixel Factory,the Grid Processing on Demand(G-POD) and the Geospatial Data Operation System-Image Processing Machine(gDos-IPM),are discussed.And the gDos-IPM,a solution for the platform of high performance computing for remote sensing,is described in detail.The gDos-IPM,which integrates the computation and storage resources and involves GPUs,multicore processors and clusters,provides the remote sensing tools about preprocessing and information extraction for massive remote sensing data in the heterogeneous computing environment.Also,it supports a dynamic model for spatio-temporal data and multi-level parallel computing.At the end of this paper,we present a thoughtful view on the challenges of HPC for further remote sensing applications.