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Implementación en GPU del algoritmo K-Means para procesamiento paralelo de imágenes de satélite disponibles en la herramienta Google Maps 9. Bibliografía [1] K. Alsabti, S. Ranka, V. Singh. An efficient K-means Clustering Algorithm. In 11th International Parallel Processing Symposium, 1998. [2] A. Plaza and C.-I. Chang, High Performance Computing in Remote Sensing, CRC Press, Boca Raton, Fla, USA, 2007. [3] R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing, Academic Press, New York, NY, USA, 2nd edition, 1997. [4] D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing, John Wiley & Sons, New York, NY, USA, 2003. [5] Smeulders, A.W.M., M. Worring, S. Santini, A. Gupta, R. Jain: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 1349–1380. [6] J. Vogel, B. Schiele: Performance evaluation and optimization for content- based image retrieval. Pattern Recognition 39 (2006) 897–909. [7] H.-W. Yoo, H.-S. Park, D.-S. Jang: Expert system for color image retrieval. Expert systems with applications 28 (2005) 347–357. [8] A. Plaza, P. Martinez, R. M. Perez, J. Plaza: A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 42 (2004) 650–663. [9] A. Plaza, J. A. Benediktsson, J. W. Boardman, et al., “Recent advances in techniques for hyperspectral image processing,” Remote Sensing of Environment, vol. 113, supplement 1, pp. S110–S122, 2009. [10] J. W. Boardman, F. A. Kruse, and R. O. Green,“Mapping Target Signatures Via Partial Unmixing of Aviris Data”, Proc. VII NASA/JPL Airborne Earth Science Workshop, pp. 23–26, 1995. [11] P.-F. Hsieh, D. Landgrebe, Classification of High Dimensional Data. Tesis Trabajo Fin de Máster -100- Sergio Bernabé García
Implementación en GPU del algoritmo K-Means para procesamiento paralelo de imágenes de satélite disponibles en la herramienta Google Maps Doctoral, School of Electrical and Computer Engineering, Purdue University, 1998. [12] S.V. Stehman, “Selecting and Interpreting Measures of Thematic Classification Accuracy”. Remote Sensing of Environment, vol. 62, pp. 77-89, 1997. [13] J. Theiler, G. Gisler, “A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation”, en: Proc. SPIE, vol. 3159, pp. 108-118, 1997. [14] J. A. Richards, Remote Sensing Digital Image Analysis: An Introduction. Springer-Verlag, Berlin, 1993. [15] G. Ball and D. Hall,“ISODATA, a novel method of data analysis and classification”. Tech. Rep. AD-699616, Stanford University, 1965. [16] S. Bernabé, A. Plaza., “A New System to Perform Unsupervised and Supervised Classification of Satellite Images from Google Maps”. SPIE Optics and Photonics, Satellite Data Compression, Communication, and Processing Conference, San Diego, CA, 2010. [17] S. Bernabé, A. Plaza, “A New Tool for Information Extraction and Mining from Satellite Imagery Available from Google Maps Engine”. 3 rd International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS´10), Valencia, Spain, 2010. [18] A. Plaza, J. Plaza, D. Valencia, “AMEEPAR: Parallel Morphological Algorithm for Hyperspectral Image Classification in Heterogeneous Networks of Workstations.” Lecture Notes in Computer Science, vol. 3391, pp. 888-891, 2006. [19] J. Setoain, M. Prieto, C. Tenllado, A. Plaza, F. Tirado, “Parallel Morphological Endmember Extraction Using Commodity Graphics Hardware,” IEEE Geoscience and Remote Sensing Letters, vol. 43, no. 3, pp. 441-445, 2007. [20] R. M. Pérez, P. Martinez, A. Plaza, P. L. Aguilar. “Systolic Array Methodology for a Neural Model to Solve the Mixture Problem”, in: Neural Trabajo Fin de Máster -101- Sergio Bernabé García
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