Unmanned Aerial Vehicle Based Agricultural Remote Sensing Multispectral Image Processing Methods

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In order to provide more flexibility in remote sensing image collection, unmanned aerial vehicle has been used to kinds of agricultural productions. Images acquired from the UAV based RS system were very useful as a result of their high spatial resolution and low turn-around time. This paper discussed general methods to process the multispectral RS data at image process level. The distortion correction caused by sensor was introduced. The geometric distortion comprised sensor distortion and external distortion caused by external parameters. At last, the general image mosaic methods were discussed.

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585-588

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April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Nowatzki, J., R. Anders, and K. Kyllo. Agricultural Remote Sensing Basics. (2004).

Google Scholar

[2] Lamb, D.W. The use of qualitative airborne multispectral imaging for managing agricultural crops – a case study in south-eastern Australia. Australian Journal of Experimental Agricultural 40: 725-38. (2001).

DOI: 10.1071/ea99086

Google Scholar

[3] Thenkabail, P.S., R.B. Smith. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71: 158-182. (2000).

DOI: 10.1016/s0034-4257(99)00067-x

Google Scholar

[4] R. Y. Tsai. A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses. IEEE Journal of Robotics and Automation RA, 3(2): 323-344. (1987).

DOI: 10.1109/jra.1987.1087109

Google Scholar

[5] Z. Y. Zhang. Flexible Camera Calibration by Viewing a Plane from Unknown Orientations. Proceedings of IEEE International Conference on Computer Vision. Colorado, USA, 1: 666-673. (1999).

DOI: 10.1109/iccv.1999.791289

Google Scholar

[6] T. Melen. Geometrical Modeling and Calibration of Video Cameras for Underwater Navigation. Norges Tekniske Hogskole Institutt for Teknisk Kybernnetikk. (1994).

Google Scholar

[7] Janne Heikkila, Olli Silven. A Four-step Camera Calibration Procedure with Implicit Image Correction. IEEE Conference on Computer Vision and Pattern Recognition, 1: 1106-1112. (1997).

DOI: 10.1109/cvpr.1997.609468

Google Scholar

[8] L. Ma, Y. Q. Chen, K. L. Moore. Analytical Piecewise Radial Distortion Model for Precision Camera Calibration. IEEE Proceedings Vision Image and Signal Processing. Atlanta, GA, USA, 153(4): 468-474. (2006).

DOI: 10.1049/ip-vis:20045035

Google Scholar

[9] A. Rosenfeld and A.C. Kak. Digital Pictrure Processing. Academic Press, Orlando, FL. (1982).

Google Scholar

[10] B. Reddy and B. Chatterji. An FFT-Based Technique for Translation, Rotation and Scale-Invariant Image Registration. IEEE Transactions on Image Processing, vol. 5, pp.1266-1271. ( 1996).

DOI: 10.1109/83.506761

Google Scholar

[11] C. Harris and M. Stephens . A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference. p.147–151. (1988).

DOI: 10.5244/c.2.23

Google Scholar

[12] S. M. Smith and J. M. Brady. SUSAN - a new approach to low level image processing. International Journal of Computer Vision 23 (1): 45–78. (1997).

Google Scholar

[13] H. Moravec . Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover. Tech Report CMU-RI-TR-3 Carnegie-Mellon University, Robotics Institute. (1980).

Google Scholar

[14] Lowe, David G. Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision. 2. p.1150–1157. (1999).

DOI: 10.1109/iccv.1999.790410

Google Scholar

[15] A. Goshtasby and G.C. Stockman. Point Pattern Matching using Convex Hull Edges. IEEE Transactions on Systems, Man, and Cybernetics, vol. 15, pp.631-637. (1985).

DOI: 10.1109/tsmc.1985.6313439

Google Scholar

[16] G. Medioni and R. Nevatia. Matching Images using Linear Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, pp.675-685. (1984).

DOI: 10.1109/tpami.1984.4767592

Google Scholar