부산대학교 도서관

The broken terrain and frequent earthquakes, together with the heavy precipitation during the rainy and typhoon seasons, pose a grave threat to slope stability in Taiwan, As a result, slope disasters are frequently found along the highways in mountainous area and seriously endanger Taiwan's lifeline of transportation and economy. The traditional approach for highway maintenance relies on patrolmen to visually screening the slopes from the ground or the patrol vehicle. Such an approach, however, requires considerable manpower and time, yet provides very limited information on spatial coverage. Lacking of an objective and quantitative comparison between the latest observations to the historical one, there is no way to diagnose the subtle yet progressive signs of slope disasters. This research employs two panorama videos of New Central Cross-Island Highway, taken on 20 April 2011 and 22 November 2011, respectively. A total of 14 sites with high risk of slope disasters are identified and selected. The multi-temporal panoramas of each site are extracted from the videos for change detection. Since the accurate GPS and IMU data were not recorded in an ordinary petrol vehicle, and these two videos were not taken from the same viewing angles along the same route, we integrate three approaches to coregister the multi-temporal panoramas. First, the adaptive enhancement is applied to the multi-temporal panoramas and scale invariant feature transform (SIFT) approach is used to generate a set of key points. These key points are examined by both the cross-correlation (CC) approach and the phase-correlation (PC) approach, with the intention to fill out those problematic points. Based on these robust key points, the PC approach is used again to generate a large number of tie points and each point is double checked with CC approach. With the large amount of accurate tie points, the multi-temporal panoramas can be accurately coregistered to meet the requirements of change detection. The results demonstrate that the difference between the coregistered multi-temporal panoramas provides reliable and quantitative information of subtle changes on highway slopes. This processing can be carried out in a fully automatic fashion, which is an innovative and low-cost approach to assess the safety of highway slopes.