Page:Advanced Automation for Space Missions.djvu/343

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TABLE6.1.-SOMESIGNIFICANT

LANDMARKS IN WORLDMODELCONSTRUCTION

Year Landmarks

1988 Autonomous on ground construction and test

of a world model directly from advanced

Landsat data

1990 Shuttle demonstrations of intelligent satellite

system begin

1990 Primitive world model for Titan mission

1992 Completed user models by opening advanced

Landsat ground test to selected user

1994 Autonomous satellite demonstration

1995 Titan intelligent demonstration mission launch

2000 Start of Intelligent Earth Sensing Information System

Table 6.1 lists a few milestones in the production of a completely autonomous and sophisticated satellite world model system. The Titan mission proposed in this report would be scheduled for launch in about 1995 and the Earth-sensing system would go into operation in 2000 AD, although a more primitive version of the world model could be ready by 1990. Since Titan is largely unknown, its world-model system must be capable of constructing a database almost entirely from first-hand on-orbit observations of the planet, hence should most properly be termed a "modeler." The Titan modeler and Earth model initially will be developed autonomously on the ground using incoming imaging data from an advanced Landsat-type satellite using conventional computers, nremory, and Space Shuttle demonstrations (Spann, 1980). Test operations will characterize the operation of world model systems, and as testing continues the Earth model portion can be opened to selected users for terrestrial applications purposes. User access will allow development of worthwhile user models for the forthcoming IESIS mission (Rich, 1979). If the world model programs are successful, launch of the Titan modeler could take place in 1995 and initiation of IESIS could begin in the year 2000. The important features in the operation of the world model arranged from its internal database through its construction, sensing, management, and user interface are: • Techniques for autonomous management of an Intelligent Satellite System • Mapping and modeling criteria for creation of a compact world model • Autonomous mapping from orbital imagery • Efficient rapid image processing techniques against world models • Advanced pattern recognition, signature analysis algorithms for multisensory data-knowledge fusion • Models of the users • Fast high density computers suitable for space environment. Autonomous hypothesis formation and natural language interfaces are important additional techniques discussed in detail in the remainder of this report, and a summary of specific recommendations of the remaining sections are in the following categories: 1. Land and ocean models 2. Earth atmosphere modeling 3. Planetary modeling 4. Data storage in space 5. Automatic mapping 6. Image processing via world model 7. Smart sensors 8. Information extraction techniques 9. Active scanning I0. Global management of complex information 11. Systems plan formation and scheduling

6.1.1 Land and Ocean Database Each world model is specific for a given mission goal. For a land-sensing Earth mission the satellite model may be as simple as a flat map with discrete "niches" specified by type, coordinates, rough boundaries, and nominal sensor and characteristic values. The niche type may be separately catalogued and a file stored of important niche characteristics, sensor combinations useful in determining boundaries between two niches, normal anomalies, and information extraction and sensor-use algorithms. Sensor combinations most useful in determining niche boundaries must be developed. The ground component of the model will be more advanced, combining finer detail, historical data, local names, seasonal and temporal information, and complex modeling equations. Oceanic (and atmospheric) components of the world nrodel will require sophisticated dynamic representation. The satellite model is the component of the world model used for direct on-board processing. Without the satellite component, it is not possible to accomplish the very large data reduction inherent in model-based systems. The satellite model must be stored so that it is compact, sensor specific, capable of updating, and consistent with its use in image processing and in the particular orbit overpass. In the image processing on board the satellite, the large number of pixel elements spanning a niche in each sensor is replaced by a small set of niche sensor characteristics such as area, average value, variance, slope, texture, etc. A highly convergent representation of desirable descriptors is required so that these few niche-dependent characteristics can faithfully represent the multitude of pixel points.


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