Abstract
The paper presents a method for patch classification and remote image segmentation based on correlated color information. During the training phase, a supervised learning algorithm is considered. In the testing phase, we used the classifier built a priori to predict which class an input image sample belongs to. The tests showed that the most relevant features are contrast, energy and homogeneity extracted from the co-occurrence matrix between H and S components. Compared to gray-level, the chromatic matrices improve the process of texture classification. For experimental results, the images were acquired by the aid of an unmanned aerial vehicle and represent various types of terrain. Two case studies have shown that the proposed method is more effective than considering separate color channels: flooded area and road segmentation. Also it is shown that the new algorithm provides a faster execution time than the similar one proposed.
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References
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610–620 (1973)
Khelifi, R., Adel, M., Bourennane, S.: Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images. EURASIP J. Adv. Signal Process. 2012, 118 (2012)
Losson, O., Porebski, A., Vandenbroucke, N., Macaire, L.: Color texture analysis using CFA chromatic co-occurrence matrices. Comput. Vis. Image Underst. 117, 747–763 (2013)
Lai, C.L., Yang, J.C., Chen, Y.H.: A real time video processing based surveillance system for early fire and flood detection. In: Instrumentation and Measurement Technology Conference, Warsaw, Poland, pp. 1–6 (2007)
Lo, S.W., Wu, J.H., Lin, F.P., Hsu, C.H.: Cyber surveillance for flood disasters. Sensors 15, 2369–2387 (2015)
Khurshid, H., Khan, M.F.: Segmentation and classification using logistic regression in remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 224–232 (2015)
Scarsi, A., Emery, W., Moser, G., Pacifici, F., Serpico, S.: An automated flood detection framework for very high spatial resolution imagery. In: Geoscience and Remote Sensing Symposium (IGARSS), pp. 4954–4957 (2014)
Ahmad, A., Tahar, K.N., Udin, W.S., Hashim, K.A., Darwin, N., Hafis, M., Room, M., Hamid, N.F.A., Azhar, N.A.M., Azmi, S.M.: Digital aerial imagery of unmanned aerial vehicle for various applications. In: IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2013), pp. 535–540 (2013)
Mnih, V., Hinton, G.E.: Learning to detect roads in high-resolution aerial images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 210–223. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15567-3_16
Kong, H., Audibert, J.Y., Ponce, J.: General road detection from a single image. IEEE Trans. Image Process. 19, 2211–2220 (2010)
He, Y., Wang, H., Zhang, B.: Color-based road detection in urban traffic scenes. IEEE Trans. Intell. Transp. Syst. 5, 309–318 (2004)
Matlab documentation. http://www.mathworks.com/products/matlab/
Popescu, D., Ichim, L.: Image recognition in UAV application based on texture analysis. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 693–704. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25903-1_60
Deza, E., Deza, M.: Dictionary of Distances. Elsevier, Amsterdam (2006)
UAV Hirrus documentation. www.aft.ro/bro.pdf
Acknowledgements
The work has been funded by Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI, project number PN-II-RU-TE-2014-4-2713.
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Popescu, D., Ichim, L., Gornea, D., Stoican, F. (2016). Complex Image Processing Using Correlated Color Information. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_63
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DOI: https://doi.org/10.1007/978-3-319-48680-2_63
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