Sensor Integration and Image Georeferencing in Support of Airborne Remote Sensing Applications




4. Airborne Remote Sensing Required Accuracy
In the previous Section, the direct georeferencing accuracy, i.e. the INS/GPS navigation accuracy at the imaging sensor PC, was discussed. However, the final remote sensing accuracy requirement is the overall accuracy obtained on the ground, i.e. when compared to accurate GCPs. Therefore, the overall accuracy is a function of the INS/GPS navigation solution, imaging sensor quality, imaging resolution, image scale, block geometry (in case of photogrammetry), type of ground coverage and the weather conditions (excluding SAR). However, the accuracy requirements have a very wide range and depend on the different applications. These requirements, expressed as RMS, are shown in Table 1. On the other hand, the general current imaging sensors capabilities in terms of obtained accuracies, after georeferencing, are summarized in Table 2. However, it should be noted here that the figures in Table 2 are quite general and may vary based on the airborne application itself, flying height, pixel size, etc. In addition, the numbers listed in Tables 1 and 2 represent the overall 3-D positional accuracy (i.e. no distinction is made for vertical or horizontal accuracies).

By inspection of Tables 1 and 2, it can be seen that the georeferenced imaging accuracy obtained by current imaging sensors meet most of the mapping accuracy requirements. However, for large scale engineering projects and cadastral mapping applications, only high precision optical frame-based cameras can provide the required specifications (taking into account low flying height and accurate DGPS positioning). Moreover, all figures are based on post-processing algorithms except the forest fire fighting application which is based on real-time processing (Add a reference for Bruce and me). Using post-processed carrier phase DGPS, typical positioning accuracy of 5-20 cm are obtained for georeferencing (rover-master distance is 10-50 km). For most current georeferencing navigation system components, a high-end tactical grade IMU is used (gyro drift of 1-3 deg/h) due to its reasonable cost (around $ 25,000 USD) and accurate post-mission attitude accuracy (0.005-0.01 deg). As mentioned earlier, an intensive research has been performed at the U of C to develop accurate airborne remote sensing systems for image direct georeferencing. Early U of C developed systems were designed to implement navigation-grade IMUs (gyro drift 0.005-0.03 deg/h) and applying post-processing, see for example Skaloud (1995); Skaloud (1999); Mostafa (1999) and Cosandier (1999). Due to the navigation-grade IMU considerable size and cost (around $ 120,000 USD), the U of C development was aimed at tactical-grade IMUs. In addition, a major research at U of C was directed to developing a real-time system for real-time applications. In the following Section, a brief description of the U of C current developed real-time airborne system for forest fire fighting is introduced and the first results of such system are discussed and analyzed.

Table 1. Required Accuracy for Different Mapping Applications (Partially after Schwarz, 1995)


Table 2. Current Georeferenced Imaging Sensor Obtained Accuracies

Closing Remarks
Direct georeferencing through the use of integrated navigation systems has developed from a topic of academic interest to a commercially viable industry in many mapping applications. Imaging and navigation hardware currently available is of such a quality that three-dimensional georeferencing can be achieved with an accuracy sufficient for many mapping tasks. The ongoing development of mathematical modeling and advanced post-mission estimation techniques will further increase accuracy and robustness of the solutions. Considerable work is needed in the areas of real-time and post-mission quality control, automation of GPS/INS integration in case of frequent lock of loss, and the efficient and user-oriented manipulation of extremely large data bases. The result of solving these problems will be an enormous extension not only of digital mapping but also its fusion with other multi-sensor data.

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