A new approach for Lidar aided fast vision

Jaan-Rong Tsay, Ming Hsiang Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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).

Original languageEnglish
Title of host publication30th Asian Conference on Remote Sensing 2009, ACRS 2009
Pages548-553
Number of pages6
Publication statusPublished - 2009 Dec 1
Event30th Asian Conference on Remote Sensing 2009, ACRS 2009 - Beijing, China
Duration: 2009 Oct 182009 Oct 23

Publication series

Name30th Asian Conference on Remote Sensing 2009, ACRS 2009
Volume1

Other

Other30th Asian Conference on Remote Sensing 2009, ACRS 2009
CountryChina
CityBeijing
Period09-10-1809-10-23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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  • Cite this

    Tsay, J-R., & Huang, M. H. (2009). A new approach for Lidar aided fast vision. In 30th Asian Conference on Remote Sensing 2009, ACRS 2009 (pp. 548-553). (30th Asian Conference on Remote Sensing 2009, ACRS 2009; Vol. 1).