Abstract
This chapter presents a pulse-coupled neural network architecture, PCNN, to segment imagery acquired with UAV images. The images correspond to normalized difference vegetation index values. The chapter describes the image analysis system design, the image acquisition elements, the original PCNN architect, the simplified PCNN, the automatic parameter setting methodology, and qualitative and quantitative results of the proposed method using real aerial images.
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References
C. A. Rokhmana.: The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia. Procedia Environmental Sciences. 24. DOI:10.1016, December 2015.
E. Salami, C. Barrado, E. Pastor.: UAV flight experiments applied to the remotes sensing of vegetated areas, Remote Sensing. 6(11), pp 11051-11081, November 2014.
H. Xiang, L. Tian.: Development of a low cost agricultural remote sensing system based on autonomous unmanned aerial vehicle (UAV), Biosystems Engineering. 108(2), pp 174-190, February 2011.
M. Hasan, J. Xiuping, A. Robles-Kelly, J. Zhou.: Multi-spectral remote sensing image registration via spatial relationship analysis on shift key points, IEEE Geoscience and Remote Sensing Symposium (IGARSS). pp 1011-1014, July 2010.
Q. Zhang, Z. Cao, Z. Hu, Y. Jia.: Joint image registration and fusion for panchromatic and multispectral images. IEEE Geoscience and Remote Sensing Letters, 12(3), pp 467-471, March 2016.
H. Erives G. J. Fitzgerald.: Automated registration of hyperspectral images for precision agriculture. Computers and Electronics in Agriculture. 47(2), pp 103-119, April 2005.
L. E. Guerra-Fernandez.: Analysis and acquisition of multispectral aerial images of agricultural fields by a UAV, M.S. Thesis, Chihuahua Institute of Technology, August 2015.
A. Vina, A. A. Gitelson, A. L. Nguy-Robertson, Y. Peng.: Comparison of different vegetation indices for the remote assessment of green leaf are index of crops. 115(12), pp. 3468-3478, September 2011.
A. K. Bhandari, a. Kumar, G. K. Singh.: Feature extraction using normalized difference vegetation index: a case study at Jabalpur City. Procedia Technology. 6(2012), pp. 612-621, (2012).
V. Poenaru, A. Badea, S. Mihai, A. Irimescu.: Multi-temporal multi-spectral and radar remote sensing for agricultural monitoring in the Braila Plain. Agriculture and Agricultural Science Procedia. 6(2015). pp. 506-516, (2015).
L. Yi, T. Qinye and F. Yingle.:Texture image segmentation using pulse couple neural networks. In: Proceedings of Industrial Electronics and Applications, 2007. pp. 355-359, September 2007.
H. Rughooputh and S. Rughooputh.: Spectral recognition using a modified Eckhorn neural network model. Image and Vision Computing. 18(14), pp. 1101-1103, (2000).
M.I. Chacon-Murguia and A. Zimmeman.: PCNNP: A pulse-coupled neural network processor. In: Proceedings of the IEEE IJCNN. pp. 1581-1585, (2002).
M.I. Chacon-Murguia and A. Zimmeman.: Image processing using the PCNN time matrix as a selective filter. In: Proceedings of the IEEE ICIP. pp. 1195-1199, (2003).
T. Lindbland and J. Kinser.: Image processing using pulse-coupled neural networks. Springer, (1998).
J.L. Johnson and M.L. Padgett.: PCNN models and applications. IEEE Transactions on Neural Networks 10(3). pp. 480-498, (1999).
J. Johnson.: Pulse-Coupled Neural Nets: Translation, rotation, scale, distortion, and intensity signal invariance for images. Applied Optics. 30(26), pp. 6239-6253, (1994).
D.G. Kuntimad and H. Ranganath: Perfect image segmentation using pulse coupled neural networks. IEEE Transactions on Neural Networks. 10(3), pp. 591-598, (1999).
E. Keller and J. Johnson.: Pulse-coupled neural network for medical analysis. In: Proceedings of SPIE Applications and science of Computation Intelligence II. pp. 444- 451, (1999).
F. Ranganath and G. Kuntimad.: Object detection using pulse coupled neural networks. IEEE Transactions on Neural Networks. 10(3), pp. 615-620, (1999).
H. Ranganath and G. Kuntimad.: Image segmentation using pulse coupled neural networks. In: Proceedings of IEEE World Congress on Computational Intelligence. pp. 1285-1290, (1994).
Y. Chen, S.K. Park, Y. Ma and R. Ala.: A new automatic parameter setting method of a simplified PCNN for image segmentation. IEEE transactions on neural networks. 22(6), pp. 880-892, (2011).
X. Deng, and Y. Ma.: PCNN Automatic parameters determination in image segmentation based on the analysis of neuron firing time. Foundations of Intelligent Systems Series Advances in Intelligent and Soft Computing.. 122(2012), pp 85-91, (2012).
H. S. Ranganath, G. Kuntimad and J. L. Johnson.: Pulse coupled neural networks for image processing. In: Proceedings of IEEE Southeast con. pp. 37-43, (1995).
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Chacon-Murguia, M.I., Guerra-Fernandez, L.E., Erives, H. (2017). UAV Image Segmentation Using a Pulse-Coupled Neural Network for Land Analysis. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_11
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