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An autonomous information generation and distribution system for the next generation of small satellites: examples of the BIRD mission experiments
Author(s): Winfried Halle; Klaus Briess; Hakan Kayal
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Paper Abstract

The general trend in remote sensing is on one hand to increase the number of spectral bands and the geometric resolution of the imaging sensors which leads to higher data rates and data volumes. On the other hand the user is often only interested in special information of the received sensor data and not in the whole data mass. Concerning these two tendencies a main part of the signal pre-processing can already be done for special users and tasks on-board a satellite. For the BIRD (Bispectral InfraRed Detection) mission a new approach of an on-board data processing is made. The main goal of the BIRD mission is the fire recognition and the detection of hot spots. This paper describes the technical solution and the first results, of an on-board image data processing system based on the sensor system on two new IR-Sensors and the stereo line scanner WAOSS (Wide-Angle-Optoelectronic-Scanner). The aim of this data processing system is to reduce the data stream from the satellite due to generations of thematic maps. This reduction will be made by a multispectral classification. For this classification a special hardware based on the neural network processor NI1000 was designed. This hardware is integrated in the payload data handling system of the satellite.

Paper Details

Date Published: 2 February 2004
PDF: 8 pages
Proc. SPIE 5234, Sensors, Systems, and Next-Generation Satellites VII, (2 February 2004); doi: 10.1117/12.512597
Show Author Affiliations
Winfried Halle, Institute of Space Sensor Technology and Planetary Exportation (Germany)
Klaus Briess, Institute of Space Sensor Technology and Planetary Exportation (Germany)
Hakan Kayal, Institute of Space Sensor Technology and Planetary Exportation (Germany)

Published in SPIE Proceedings Vol. 5234:
Sensors, Systems, and Next-Generation Satellites VII
Roland Meynart; Joan B. Lurie; Steven P. Neeck; Michelle L. Aten; Haruhisa Shimoda, Editor(s)

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