Abstract
GIS applications involve applying classification algorithms to remotely sensed images to determine information about a specific region on the Earth’s surface. These images are very useful sources of geographical data commonly used to classify land cover, analyze crop conditions, assess mineral and petroleum deposits and quantify urban growth. In this paper, we propose a semantic supervised clustering approach to classify multispectral information in satellite images. We use the maximum likelihood method to generate the clustering. In addition, we complement the analysis applying spatial semantics to determine the training sites and refine the classification. The approach considers the a priori knowledge of the remotely sensed images to define the classes related to the geographic environment. In this case, the properties and relations that involve the geo-image define the spatial semantics; these features are used to determine the training data sites. The method attempts to improve the supervised clustering, adding the intrinsic semantics of multispectral satellite images in order to establish the classes that involve the analysis with more precision.
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References
Unsalan, C., Boyer, K.L.: Classifying land development in high-resolution Satellite imagery using hybrid structural-multispectral features. IEEE Transactions on GeoScience and Remote Sensing 42(12), 2840–2850 (2004)
De Vleeschouwer, C., Delaigle, J.F., Macq, B.: Invisibility and application functionalities in perceptual watermarking – an overview. Proceedings of the IEEE 90(1), 64–77 (2002)
Heileman, G.L., Yang, Y.: The Effects of Invisible Watermarking on Satellite Image Classification. In: Proceedings of the ACM Workshop on Digital Rights Management, Washington, DC, USA, pp. 120–132 (2003)
Torres, M., Levachkine, S.: Ontological representation based on semantic descriptors applied to geographic objects. Revista Iberoamericana Computacióny Sistemas 12(3), 356–371 (2009)
Nishii, R., Eguchi, S.: Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods. IEEE Transactions on GeoScience and Remote Sensing 43(11), 2547–2554 (2005)
Bandyopadhyay, S.: Satellite image classification using genetically guided fuzzy clustering with spatial information. International Journal of Remote Sensing 26(3), 579–593 (2005)
Gómez-Chova, L., Bruzzone, L., Camps-Valls, G., Calpe-Maravilla, J.: Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel. In: Proceedings of Geoscience and Remote Sensing Symposium (IGARSS), Boston, MA, USA, vol. 4, pp. 391–394 (2008)
Granville, V., Rasson, J.P.: Bayesian filtering and supervised classification in image remote sensing. Computational Statistics & Data Analysis 20(2), 203–225 (1995)
Neg, I., Kittler, J., Illingworth, J.: Supervised segmentation using a multiresolution data representation. Signal Processing 31(2), 133–163 (1993)
Keuchel, J., Naumann, S., Heiler, M., Siegmund, M.: Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sensing of Environment 86(4), 530–541 (2003)
Brown, D.G., Lusch, D.P., Duda, K.A.: Supervised classification of types of glaciated landscapes using digital elevation data. Geomorphology 21(3-4), 233–250 (1998)
Krooshof, P., Postma, G.J., Melssen, W.J., Buydens, L.M., Tranh, T.N.: Effects of including spatial information in clustering multivariate image data. Transactions of Trends in Analytical Chemistry 25(11), 1067–1080 (2006)
Giacinto, G., Roli, F., Bruzzone, L.: Combination of neural and statistical algorithms for supervised classification of remote-sensing images. Pattern Recognition Letters 21(5), 385–397 (2000)
Anzalone, A., Bizzarri, F., Parodi, M., Storace, M.: A modular supervised algorithm for vessel segmentation in red-free retinal images. Computers in Biology and Medicine 38, 913–922 (2008)
Atkinson, P.M.: Spatially weighted supervised classification for remote sensing. International J. of Applied Earth Observation and Geoinformation 5, 277–291 (2004)
Bandyopadhyay, S., Maulik, U., Pakhira, M.K.: Clustering using simulated annealing with probabilistic redistribution. International Journal of Pattern Recognition and Artificial Intelligence 15(2), 269–285 (2001)
Chuvieco, E., Huete, A.: Fundamentals of Satellite Remote Sensing. CRC Press, Boca Raton (2010)
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Torres, M., Moreno, M., Menchaca-Mendez, R., Quintero, R., Guzman, G. (2010). Semantic Supervised Clustering Approach to Classify Land Cover in Remotely Sensed Images. In: Kim, Th., Pal, S.K., Grosky, W.I., Pissinou, N., Shih, T.K., Ślęzak, D. (eds) Signal Processing and Multimedia. MulGraB SIP 2010 2010. Communications in Computer and Information Science, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17641-8_10
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DOI: https://doi.org/10.1007/978-3-642-17641-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17640-1
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