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An Autonomous Information Generation and Distribution System for the Next Generation of Small Satellites Examples of the BIRD Mission Experiments

机译:鸟类特派团实验下一代小型卫星实验的自主信息生成与分配系统

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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.
机译:在遥感总的趋势是一方面增加光谱带的数目和所述成像传感器的几何分辨率,这导致更高的数据速率和数据量。在另一方面,用户往往只关心所接收的传感器数据的特殊信息,而不是在整个数据质量。关于这两种倾向的信号预处理的主要部分已经可以为特殊用户和板载一个satellite.For鸟(双频红外探测)任务的机载数据处理的新方法任务做完而成。鸟飞行任务的主要目的是火识别和热点的检测。本文描述的技术方案和第一结果,基于传感器系统上的两个新的IR传感器和立体声线路扫描器WAOSS(广角光电-扫描仪)的车载图像数据处理系统的。该数据处理系统的目的是从卫星数据流减少由于专题地图的世代。这将减少由多光谱分类进行。对于这种分类基于神经网络处理器NI1000一种特殊的硬件设计。这种硬件集成在卫星的有效载荷数据处理系统。

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