TY - GEN
T1 - A new approach for Lidar aided fast vision
AU - Tsay, Jaan-Rong
AU - Huang, Ming Hsiang
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Facets stereo vision (FAST Vision) is an image matching method, in which digital surface model (DSM) generation and ortho image computation are simultaneously done. DSM can also be generated by using airborne LiDAR points, from which LiDAR points on the interest surface must be first selected. It is often aided by manual image interpretation. Also, the thus interpolated DSM might have large interpolation errors due to the lack of break lines and larger interval between two neighboring laser scanning lines. This paper proposes a new approach for LiDAR aided FAST Vision. Test results demonstrate that FAST Vision is very available for accurate image matching, DSM determination and ortho image computation in areas with continuous gray-level surface and continuous geometric surface (DSM). FAST Vision becomes fast (less computation time) and more reliable after integration of LiDAR points. The a posteriori σ̂ 0 is reduced, after LiDAR points are integrated into FAST Vision. The root mean square values of dZ i (= Z i FV - Z i LiDAR), i, are also reduced from RMSD=17.969m (without LiDAR points) to RMSD=6.378m and 5.970m (with LiDAR points).
AB - Facets stereo vision (FAST Vision) is an image matching method, in which digital surface model (DSM) generation and ortho image computation are simultaneously done. DSM can also be generated by using airborne LiDAR points, from which LiDAR points on the interest surface must be first selected. It is often aided by manual image interpretation. Also, the thus interpolated DSM might have large interpolation errors due to the lack of break lines and larger interval between two neighboring laser scanning lines. This paper proposes a new approach for LiDAR aided FAST Vision. Test results demonstrate that FAST Vision is very available for accurate image matching, DSM determination and ortho image computation in areas with continuous gray-level surface and continuous geometric surface (DSM). FAST Vision becomes fast (less computation time) and more reliable after integration of LiDAR points. The a posteriori σ̂ 0 is reduced, after LiDAR points are integrated into FAST Vision. The root mean square values of dZ i (= Z i FV - Z i LiDAR), i, are also reduced from RMSD=17.969m (without LiDAR points) to RMSD=6.378m and 5.970m (with LiDAR points).
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M3 - Conference contribution
AN - SCOPUS:84866135818
SN - 9781615679843
T3 - 30th Asian Conference on Remote Sensing 2009, ACRS 2009
SP - 548
EP - 553
BT - 30th Asian Conference on Remote Sensing 2009, ACRS 2009
T2 - 30th Asian Conference on Remote Sensing 2009, ACRS 2009
Y2 - 18 October 2009 through 23 October 2009
ER -