(2) RPM on Low-Resolution Stage 
    
    
    
  
RPM SES W/O RPM 
(3) RPM on High-Resolution Stage 
W/O RPM RPM 
(4) RPM with DTM reference 
DTM REF RPM 
Figure 8 Changes of the DEM 
under the coarse-to-fine approach. 
Aerial photo by GSI Japan (2008). 
4. CONCLUSION 
In this research, an RPM is proposed for an improvement in the 
accuracy of the DEM. The RPM is a method for evaluating the 
reliability of DEM points by projecting to neighbor stereo 
models and removing the mismatched points generated during 
the matching process. 
Evaluations of the RPM were performed for five areas under 
various photo conditions. A quantitative evaluation was 
performed by comparing the airborne lidar data and the DEM 
extracted through stereo matching, while a qualitative 
evaluation was performed using a color-shaded relief map. The 
results confirm the effectiveness of the RPM. In particular, it 
was confirmed that the use of the RPM with all stereo models, 
or with stereo models of the previous and following strips only, 
is effective for an improvement in accuracy. 
Furthermore, the RPM was successful in solving the problem of 
non-removed mismatched points generated during half of the 
processing when using the coarse-to-fine. The success of the 
RPM in improving the reliability of the matching process, while 
maintaining a fast search, shows the merit of the coarse-to-fine 
approach. 
The RPM achieved these results by re-using the stereo models 
generated by traditional aerial photogrammetry along and 
across the strip. That is, these results are achieved without 
generating further processing times for creating new stereo 
models, which is a problem in multiple viewpoint matching. 
In particular, this research offered the following original ideas 
to optimize this process. 
(1) The use of stereo models of the previous and 
following strips for evaluating the reliability of the 
DEM points. 
(2)  Restricting the process during the high-resolution 
stage in the coarse-to-fine approach, and referring to 
the DTM to remove large mismatched points prior to 
the matching process. 
Based on the originality of this method, the accuracy of the 
DEM is expected to improve. This method is also expected to 
contribute toward optimizing the preparation of special 
information, with a tendency toward higher accuracy, higher 
density, and a wider area. 
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