A Vision Based Forced Landing Site Selection System for an Autonomous UAV

This paper presents a system overview of the UAV forced landing site selection system and the results to date. The forced landing problem is a new field of research for UAVs and this paper will show the machine vision approach taken to address this problem. The results are based on aerial imagery collected from a series of flight trials in a Cessna 172. The aim of this research is to locate candidate landing sites for UAV forced landings, from aerial imagery. Output image frames highlight the algorithm's selected safe landing locations. The algorithms for the problem use image processing techniques and neural networks for the classification problem. The system is capable of locating areas that are large enough to land in and that are free of obstacles 92.3% ± 2% (95% confidence) of the time. These areas identified are then further classified as to their surface type to a classification accuracy of 90% ± 3% (98% confidence). It should be noted that although the system is being designed primarily for the forced landing problem for UAVs, the research can also be applied to forced landings or glider applications for piloted aircraft.

[1]  DeLiang Wang,et al.  Texture classification using spectral histograms , 2003, IEEE Trans. Image Process..

[2]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[3]  Lianping Chen,et al.  Effects of different Gabor filters parameters on image retrieval by texture , 2004, 10th International Multimedia Modelling Conference, 2004. Proceedings..

[4]  M. A. Shaban,et al.  Textural classification of high resolution digital satellite imagery , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[5]  S. Herman,et al.  Locally-adaptive processing of television images based on real-time image segmentation , 2002, 2002 Digest of Technical Papers. International Conference on Consumer Electronics (IEEE Cat. No.02CH37300).

[6]  Jiebo Luo,et al.  Probabilistic spatial context models for scene content understanding , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[7]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[8]  P. Bacchetti,et al.  Sample size calculations in clinical research. , 2002, Anesthesiology.

[9]  Abraham Kandel,et al.  A genetic fuzzy neural network for pattern recognition , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[10]  Slobodan Ribarić,et al.  Introduction to Pattern Recognition , 1988 .

[11]  Jeff Berens,et al.  Image indexing using compressed colour histograms , 2000 .

[12]  Anil K. Jain,et al.  Detecting sky and vegetation in outdoor images , 1999, Electronic Imaging.

[13]  Vittorio Murino,et al.  Structured neural networks for pattern recognition , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Anil K. Jain,et al.  Introduction to Pattern Recognition , 2007 .

[15]  Duncan A. Campbell,et al.  A Vision Based Emergency Forced Landing System for an Autonomous UAV , 2005 .

[16]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Nasser Kehtarnavaz,et al.  Proceedings of SPIE - The International Society for Optical Engineering , 1991 .

[18]  Hubert Cardot,et al.  Graph of neural networks for pattern recognition , 2002, Object recognition supported by user interaction for service robots.

[19]  Ashfaq A. Khokhar,et al.  Scalable Color Image Indexing and Retrieval Using Vector Wavelets , 2001, IEEE Trans. Knowl. Data Eng..

[20]  Anil K. Jain,et al.  Content-based hierarchical classification of vacation images , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[21]  C.S. Lindquist,et al.  Use of adaptive segmentation and classification algorithms in satellite imagery , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[22]  Rodney A. Walker,et al.  Classification of Candidate Landing Sites for UAV Forced Landings , 2005 .