Overlap Analysis of the Images from Unmanned Aerial Vehicles

Compared with traditional images, high-resolution images obtained by unmanned aerial vehicles (UAV) are superior in many aspects such as low cost, convenience and so on. However, the attitude of unmanned aerial vehicles cannot be determined accurately, which will lead to the decrease of accuracy and efficiency of automatic matching, so image preprocessing should be done to guarantee enough degree of overlap for image mosaicking and mapping. There are some ascendant characteristics of SIFT operator such as invariant to scale, rotation, and lightness, and these characteristics are very useful in increasing the matching accuracy of UAV images. In this paper, the overlap analysis of UAV images has been processed based on the SIFT algorithm, and the steps of overlap analysis are described, and the way of treating wrong matching points is explained. Finally, satisfactory results are obtained.

[1]  Jorge Lobo,et al.  The Coalition Policy Management Portal for Policy Authoring, Verification, and Deployment , 2008, 2008 IEEE Workshop on Policies for Distributed Systems and Networks.

[2]  Fabio Martinelli,et al.  A model for usage control in GRID systems , 2007, 2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops - SecureComm 2007.

[3]  Robin Milner,et al.  On Observing Nondeterminism and Concurrency , 1980, ICALP.

[4]  Alberto Verdejo,et al.  Implementing CCS in Maude 2 , 2002, Electron. Notes Theor. Comput. Sci..

[5]  Emil C. Lupu,et al.  PAES: Policy-Based Authority Evaluation Scheme , 2009, DBSec.

[6]  Matthew A. Brown,et al.  Multi-image matching using invariant features , 2005 .

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  C. A. R. Hoare,et al.  Communicating sequential processes , 1978, CACM.

[9]  Kaarel Kaljurand,et al.  ATTEMPTO CONTROLLED ENGLISH AS A SEMANTIC WEB LANGUAGE , 2007 .

[10]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[11]  Tony Lindeberg,et al.  Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention , 1993, International Journal of Computer Vision.

[12]  Robin Milner,et al.  Communication and concurrency , 1989, PHI Series in computer science.

[13]  Álvaro Enrique Arenas,et al.  Controlling Usage in Business Process Workflows through Fine-Grained Security Policies , 2008, TrustBus.

[14]  Álvaro Enrique Arenas,et al.  Towards Modelling Obligations in Event-B , 2008, ABZ.

[15]  Martín Abadi,et al.  Logic in access control , 2003, 18th Annual IEEE Symposium of Logic in Computer Science, 2003. Proceedings..

[16]  Colin Stirling,et al.  Modal and Temporal Logics for Processes , 1996, Banff Higher Order Workshop.

[17]  Ilaria Matteucci,et al.  CNL4DSA: a controlled natural language for data sharing agreements , 2010, SAC '10.

[18]  Rocco De Nicola,et al.  Programming Access Control: The KLAIM Experience , 2000, CONCUR.

[19]  Wang Zhen.  On the Key Technique of Fast Monitoring System for Land Resourses Unmanned Plane Remote Sensing , 2008 .

[20]  Benjamin Aziz,et al.  Methodologies and tools for data sharing agreement infrastructure , 2008 .

[21]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[22]  Arnon Rosenthal,et al.  A Data Sharing Agreement Framework , 2006, ICISS.

[23]  Jorge Lobo,et al.  Expressive policy analysis with enhanced system dynamicity , 2009, ASIACCS '09.