Abstract
Unmanned Aerial Vehicles (UAV) are used to conduct a variety of recognition as well as specific missions such as target tracking, safe landing. The transmitted image sequences to be interpreted at ground station usually face limited requirements of the data transmission. In this paper, on one hand, we handle a surveillance mission with segmenting a UAV video’s content into semantic regions. We deploy a spatio-temporal framework that considers UAV videos specific characteristics for segmenting multi regions of interest. After post-processing steps on the segmentation results, a support vector machine classifier is used to recognize regions. In term of temporal feature, we combine the results from the previous frames by proposing to use a state transition formulating through a Markov model. On the other hand, this study also assesses the influences of data reduction techniques on the proposed techniques. The comparisons between the untreated configuration and control conditions under manipulations of the frame rate, spatial resolution, and compression ratio, demonstrate how these data reduction techniques adversely influence the algorithm’s performance. The experiments also point out the optimal configuration in order to obtain a trade-off between the target performance and limitation of the data transmission.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Muller, S., Zaum, D.: Robust building detection in aerial images. In: Proceedings of ISPRS Workshop CMRT 2005, Vienna, pp. 143–148 (2005)
Forlani, G., Nardinocchi, C., Scaioni, M., Zingaretti, P.: Complete classification of raw LIDAR data and 3D reconstruction of buildings. Pattern Anal. Appl. 8(4), 357–374 (2006)
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28(5), 823–870 (2007)
Malinverni, E.S., Tassetti, A.N., Mancini, A.: Hybrid object-based approach for land use/land cover mapping using high resolution imagery. Int. J. Geog. Inf. Sci. - IJGIS 25(6), 1025–1043 (2011)
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Soulié, F.F. (ed.) Neurocomputing, pp. 41–50. Springer, Heidelberg (1990)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)
Chih-Chung, C., Chih-Jen, L.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)
Acknowledgements
This work is supported by Hanoi University of Science and Technology (HUST), under grant reference number T2015-096.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vu, H., Le, T.L., Nguyen, V.G., Dinh, T.H. (2016). Selections of Suitable UAV Imagery’s Configurations for Regions Classification. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_77
Download citation
DOI: https://doi.org/10.1007/978-3-662-49381-6_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49380-9
Online ISBN: 978-3-662-49381-6
eBook Packages: Computer ScienceComputer Science (R0)