at the satellite, but before the sensor transduces visual photons into a signal). This extracted information is called "satellite local reality." If satellite local reality is not obtained, then an interpretation correlation problem arises when different sensors are used at various times to observe the same thing.
Once satellite local reality has been determined, it must be mapped onto an "object local reality" where "object" refers to the surface or volume under observation. For example: suppose that the state of the relatively clear atmosphere and a power spectrum at several chosen frequencies for a particular image pixel element of the Earth's surface are accurately known. The theory of observation must be able to use the world model (in this case the atmospheric component of the model) to determine which parts of the satellite local power spectrum are generated from a ground effect, and which are atmospheric phenomena. Reflection, refraction, absorption and emission cannot, in general, be clearly separated. Thus, the attempt to translate satellite local to object local descriptions requires some additional information.
The predictive aspects of the world model are important here. The model can predict much of the satellite local power spectrum, so inversion to object local spectrum need not be performed at all except in specific (and hopefully, relatively infrequent) cases where observations deviate significantly from prediction. These deviations are called anomalies. When an anomaly occurs, there may be simple alternative world states IESIS can hypothesize in an attempt to find an explanation for the anomaly. In this mode of action the model is altered and a new prediction for the observation generated. If a reasonable world model alteration leads to a predicted image that matches that actually observed, then the altered model may represent an adequate estimate of current reality. Other specific observations should be designed with the objective of testing the new hypothesis.
If an anomaly cannot be disposed of by hypothesizing new world states, alternative mechanisms are needed. It is desirable to proceed as far as possible without explicit human intervention. One alternative is to automatically schedule other observational configurations (different satellites, sensors, or lighting conditions) to gather enough satellite local information and permit clear computation of object local signature. Once this is accomplished the final interpretation must be made which involves mapping object local signatures into a state description suitable for incorporation into the world state component of the world model (see section 2.3).
The role of a predictive model in efficient image gathering is essential. Even when it is theoretically possible to clearly map satellite local reality into a high-level description of object local reality without such a model, model use can significantly increase the efficiency of the observation and the speed of the interpretation process. The following is a partial list of the many ways world models may be helpful:
? Extending the range of possible viewing conditions under which usable information can be gathered (e.g., compensating for variations in sun angle) ? Predicting when certain types of observations are impossible because of unfavorable viewing conditions which can be known prior to the time of observation ? Computing the least costly set of sensors (e.g., fewest sensors for shortest time, or use of sensors which at the particular time have no other demands on them) needed to determine a particular fact about the world ? Avoiding taking certain new observations by deriving at least some responses to requests from information already in the world model database.
IESIS is not oriented toward the storage of information as images. Rather, images are processed in real time (or almost real time) and only extracted information is stored in the world model. In such a context, the emphasis shifts from finding observation strategies which yield absolutely unique sensor signatures for identifying the condition of the world, to observing only what is necessary to identify the state of the world in the context of the world model. The theory of observation can be viewed as part of the world model, and represents a large part of the knowledge necessary to connect sensor-encoded information with more human-oriented descriptions of reality contained in the world model database.
2.2.7 System Flexibility
It is very difficult to anticipate the entire range of users to whom Earth-sensing systems may be applicable. IESIS must be flexible enough to allow a scale-up of total system throughput to accommodate a growing number of customers. Similarly, it is unlikely that the mission system will have available, by the year 2000, the ultimate in sensor technology. Almost certainly, a rapid evolution of ideas and technology will occur after a short period of system use. If the system is not to be the seed of its own rapid obsolescence, it is imperative that it be flexible enough to accommodate new modes of observation including new equipment and new processing procedures.
The general philosophy of providing a flexible information system for the sophisticated user virtually demands that the user be able to specify new algorithms for controlling the data accumulation and data-analysis processes. User-defined data collection control becomes important for the advanced user when observation scheduling must be sensitive to dynamic events in real time - where it would be impractical or impossible to use the standard system- scheduling mechanisms. Such individuals may require specialized data interpretation processing for a variety of
