Abstract
Abstract. In the last decade, the remote sensing community has observed a significant growth in number of satellites, sensors and their resolutions, thereby increasing the volume of data to be processed each day. Satellite data processing is a complex and time consuming activity. It consists of various tasks, such as decode, decrypt, decompress, radiometric normalization, stagger corrections, ephemeris data processing for geometric corrections etc., and finally writing of the product in the form of an image file. Each task in the processing chain is sequential in nature and has different computing needs. Conventionally the processes are cascaded in a well organized workflow to produce the data products, which are executed on general purpose high-end servers / workstations in an offline mode. Hence, these systems are considered to be ineffective for real-time applications that require quick response and just-intime decision making such as disaster management, home land security and so on. This paper discusses anovel approach to processthe data online (as the data is being acquired) using a heterogeneous computing platform namely XSTREAM which has COTS hardware of CPUs, GPUs and FPGA. This paper focuses on the process architecture, re-engineering aspects and mapping of tasks to the right computing devicewithin the XSTREAM system, which makes it an ideal cost-effective platform for acquiring, processing satellite payload data in real-time and displaying the products in original resolution for quick response. The system has been tested for IRS CARTOSAT and RESOURCESAT series of satellites which have maximum data downlink speed of 210 Mbps.
Highlights
Remote Sensing (RS) community has observed a significant growth in number of satellites, sensors and their resolutions, causing an exponential growth in the volume of data to for processing each day during last decade
Time Stamping: Time stamp each frame with the Ground Reception Time (GRT), which is read from Time Code Translator (TCT)
Satellite payload data processing to generate level-1A product involves sequence of complex multiple tasks that need to be executed one after the other. They are done on high server(s) in a sequential mode which is suboptimal and introduces substantial delays in actual usage of the data, thereby is not suitable for applications needing quick response
Summary
Remote Sensing (RS) community has observed a significant growth in number of satellites, sensors and their resolutions, causing an exponential growth in the volume of data to for processing each day during last decade. On the other hand there has been a growing demand to cut down the lead time for generating the products that need to be used by different applications, such as disaster management, home land security, etc. That demand quick response; the data products need to be made available for these applications in the quickest possible time. For some of the missions such as Cartosat series of satellites, stagger estimation and stagger correction are done before writing of the product onto the disk. IRS Satellites such as IRS Cartosat-1[3],Cartosat-2[4], Resourcesat[5] capture images of ground and transmit the image data ( referred to as video data) after performing various onboard processing such as compression, encryption, error correction coding etc. Along with the image data, data pertaining to various sensors on-board, such as Star Sensor, Gyros, clock, health parameters etc., which are referred to as auxiliary data (ephemeris data or AUX data ) are multiplexed with video data and transmitted to ground stations
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