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Failure prediction in automatically generated digital elevation models
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DOI：10.1016/S0098-3004(00)00129-1.png)
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Abstract
En 中文
Developments in digital photogrammetry have provided the ability to generate digital elevation models (DEMs) automatically and are increasingly used by geoscientists. Using overlapping imagery, dense grids of digital elevations can be collected at high speeds (150 points per second) with a high level of accuracy. The trend towards using PC-based hardware, the widespread use of geographical information systems, and the forthcoming availability of high-resolution satellite imagery over the Internet at ever lower costs mean that the use of automated digital photogrammetry for elevation modelling is likely to become more widespread. Automation can reduce the need for an in-depth knowledge of the subject thus rendering the technology an option for more users. One criticism of the trend towards the automated black box approach is the lack of quality control procedures within the software, particularly with reference to identifying areas of the DEM with low accuracy. The traditional method of accuracy assessment is through the use of check point data (data collected by an independent method which has a higher level of accuracy against which the DEM can be compared). Check point data are, however, rarely available and it is typically recommended that the user manually check and edit the data using stereo viewing methods, a potentially lengthy process which can negate the obvious speed advantages brought about by automation. A data processing model has been developed that is capable of identifying areas where elevations are unreliable and to which the user should pay attention when editing and checking the data. The software model developed will be explained and described in detail in the paper. Results from tests on different scales of imagery, different types of imagery and other software packages will also be presented to demonstrate the efficacy and significantly the generality of the technique with other digital photogrammetric software systems. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keywords:
digital photogrammetry
DEM
accuracy
classification
strategy parameters
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