Abstract
This paper proposes a method, that integrates image analysis and probabilistic techniques, for counting olive trees in high-resolution satellite images. Counting trees becomes significant for surveying and inventorying forests, and in certain cases relevant for assessing estimates of the production of plantations, as it is the case of the olive trees fields. The method presented in this paper exploits the particular characteristics of parcels, i.e. a certain reticular layout and a similar appearance of trees, to yield a probabilistic measure that captures the confident of each spot in the image to be an olive tree. Some promising experimental results have been obtained in satellite images taken from QuickBird.
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References
Blazquez, C.H.: Computer-Based Image Analysis and Tree Counting with Aerial Color Infrared Photography. Journal of Imaging Technology 15(4), 163–168 (1989)
Brandtberg, T., Walter, F.: Automated delineation of individual tree crown in high spatial resolution aerial images by multiple-scale analysis. Machine Vision and Applications 11, 64–73 (1998)
Firestone, L., Rupert, S., Olson, J., Mueller, W.: Automated Feature Extraction: The Key to Future Productivity. Photogrammetric Engineering and Remote Sensing 62(6), 671–674 (1996)
Gonzalez, R.C., Woods, R.E.: Digital image processing. Addison-Wesley, Reading, Mass (1987)
Butt, M.A., Maragos, P.: Optimal design of chamfer distance transforms. IEEE Transactions on Image Processing 7, 1477–1484 (1998)
Gruen, A., Baltsavias, E.P., Henricsson, O.: Automatic Extraction of Man-Made Objects from Aerial and Space Images (II). Birkhäuser Verlag, Basel (1997)
Karantzalos, K.G., Argialas, D., Georgopoulos, A.: Towards coastline detection from aerial imagery. In: Int. Conf. of Image and Signal Processing for Remote Sensing VII, Crete, Greece
Karantzalos, K.G., Argialas, D.: Towards Automatic Olive Tree Extraction from Satellite Imagery. Geo-Imagery Bridging Continents. XXth ISPRS Congress, Istanbul, Turkey (July 12-23, 2004)
Kay, S., Leo, O., Peedell, S.: Computer-assisted recognition of Olive trees in digital imagery. In: ESRI User Conference, San Diego (July 27-31, 1999)
Ko, C.C., Lin, C.S., Huang, J.P., Hsu, R.C.: Automatic Identification of Tree Crowns in Different Topology. In: Proc. of Visualization, Imaging, and Image Processing, Benidorm, Spain (2005)
Strozzi, T., Kaab, A., Frauenfelder, R., Wegmuller, U.: Detection and monitoring of unstable high-mountain slopes with L-band SAR interferometry Geoscience and Remote Sensing Int. Symp. pp. 1852–1854 (July 21-25, 2003)
Pollock, R.J.: The automatic recognition of individual trees in aerial images of forest based on a synthetic tree crown image model. In: 1st International Airborne Remote Sensing Conference and Exhibition, France (1996)
Open Source Computer Vision Library. http://www.sourceforge.net/projects/opencvlibrary
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© 2007 Springer-Verlag Berlin Heidelberg
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González, J., Galindo, C., Arevalo, V., Ambrosio, G. (2007). Applying Image Analysis and Probabilistic Techniques for Counting Olive Trees in High-Resolution Satellite Images. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_84
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DOI: https://doi.org/10.1007/978-3-540-74607-2_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
Online ISBN: 978-3-540-74607-2
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