Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
A Fast Adaptive Content-based Retrieval System of Satellite Images Database using Relevance Feedback

Authors: Hanan Mahmoud Ezzat Mahmoud, Alaa Abd El Fatah Hefnawy


In this paper, we present a system for content-based retrieval of large database of classified satellite images, based on user's relevance feedback (RF).Through our proposed system, we divide each satellite image scene into small subimages, which stored in the database. The modified radial basis functions neural network has important role in clustering the subimages of database according to the Euclidean distance between the query feature vector and the other subimages feature vectors. The advantage of using RF technique in such queries is demonstrated by analyzing the database retrieval results.

Keywords: content-based image retrieval, large database of image, RBF neural net, relevance feedback

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1074


[1] John A. Richards and Xiuping Jia, "Remote Sensing Digital Image Analysis: an introduction" third edition pp 1-73 2000.
[2] Milan Sonka, Vaclav Hlavac, Roger Boyle "Image Processing Analysis and Machine Vision" second edition pp 290-361, 1999.
[3] Floyd F. Sabins, "Remote Sensing, principles and interpretation: land use and land cover" third edition pp387-416, 1999.
[4] Adeli,H., and Hung, S., John Wily, "Machine Learning- Neural Networks, Genetic Algorithms, and Fuzzy Systems" 1995.
[5] Bose, N.K., and Liang, P.,"Neural Networks Fundamentals with Graphs, Algorithms, and Application", Mc-GrawHill,NY.1996.
[6] Lipmann,R.P."An introduction to computing with Neural Networks" IEEE ASSP Magazine.1991.
[7] Benediktsson,J.A., Swain, P.H., and Erosy, O. "Neural netrorkApproaches versus Statistical methods in classification of Multisource Remote sensing Data", IEEE Trans. Geoscience and Remote sensing,vol. 28,no.4,pp540-552.
[8] Paul L. Rosin and Freddy Fierens."Improving Neural Network Grnralization" In Proceedings of IGARSS'95, 1995.
[9] Krishna Mohan Buddhiraju "Contextual Refinement of neural network classification using Relaxation Labelling Algorithms"22nd Asian Conference on Remote sensing.November 2001.
[10] Chen, D.,D. Stow, and P Gong, "Examining the effect of spatial resolution on classification accuracy : an urban environmental case " International Journal of Remote Sensing 25(00):1-16, 2004.
[11] Chen, D.,D. Stow, and P Gong, "Strategies for integrating information from multiple spatial resolutions into land use/land cover classification routines" Photogrammetric Engineering & Remote Sensing ,69(11):1279-1287,2003.
[12] Chen, D.,D. Stow, and P Gong, "the effect of training strategies on supervised classification at different spatial resolutions Photogrammetric Engineering & Remote Sensing ,68(11):1155-1162.
[13] Foody, G.M.,"Status of land cover classification accuracy assessment" Remote Sensing of Environmental 80 pp.185-201.
[14] Mc Cullac and Pits "Artificial Neural Networks :An Introduction - Radial Basis Function Neural Network" Chapter7, pp 236-284,1995.
[15] T.Bretschneider,O.Kao, "A Retrieval System For Remotely sensed Imagery" NASA EPSCoR, 2000.
[16] Koki Iwao, Taizo Yamamoto,Ryosuke, Shiro Ochi, and Mitsuharu Tokunaga "Automatic Database Development Methods for a very large number of satellite images " spatial information science, Tokyo University, 2001.
[17] Y. Rui, T.S.Huang, "A novel relevance feedback technique in image retrieval" in Proceedings ACM Multimedia'99 (Part2), Orlando, FL, USA, pp.67-70, 1999.
[18] Z. Jin, I. King, X.Q. Li, " Content-Based image retrieval by relevance feedback" in Proceedings of the fouth international Conference on visual information systems, Lecture notes in computer science, Vol.1929, Lyon, France, November 2-4 Springer, Berlin, 2000.