Abstract
There has been a focus on developing image indexing techniques which have the capability to retrieve image based on their contents. The main feature extraction methods are content Based Image Retrieval (CBIR) also known as query by Image content (QBIC). This paper presents a technique to derive the colors, shapes, textures, or any other information that can be derived from a satellite image Using Texture filters and realizing it with artificial neural networks. This image processing technique are been utilized to identify important urban features such as buildings and gardens and rural features such as natural vegetation, water bodies, and fields. Textures are represented by Texel, which are then placed into a number of sets, depending on how many textures are detected in the image.
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References
Chen, S., Mulgrew, B., Grant, P.M.: A clustering technique for digital communications channel equalization using radial basis function networks. IEEE Trans. on Neural Networks 4, 570–578 (1993)
Duncombe, J.U.: Infrared navigation—Part I: An assessment of feasibility. IEEE Trans. Electron Devices ED-11, 34–39 (1959)
Lin, C.Y., Wu, M., Bloom, J.A., Cox, I.J., Miller, M.: Rotation, scale and translation resilient public watermarking for images. IEEE Trans. Image Process. 10(5), 767–782 (2001)
Ardö, J., Pilesjö, P., Skidmore, A.: Neural networks, multi temporal Landsat The-matic Mapper data and topographic data to classify forest damages in the Czech Republic. Canadian Journal of Remote Sensing 23(3), 217–229 (1997)
Rajani, T., Mangala, Bhirud, S.G.: An Effective ANN-Based Classification System for Rural Road Extraction in Satellite Imagery. European Journal of Scientific Research 47(4), 574–585 (2010) ISSN 1450-216X
Boggess, J.E.: Identification of Roads in Satellite Imagery Using Artificialneural Networks: Acontextual Approach. Computer Science Department, vol. (601), pp. 2325–2756. Mississippi State University, P. O. Drawer CS, Mississippi State, MS 39762, U.S.A
Rekik, A., Zribi, M., Hamida, A.B., Benjelloun, M.: An Optimal Unsupervised Satellite image Segmentation Approach Based on Pearson System and k-Means Clustering Algorithm Initialization. International Journal of Signal Processing 5(1), 38–45 (2009)
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© 2013 Springer India
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Karthikeya Sharma, T., Sarvesh Babu, N.S., Mamatha, Y.N. (2013). Satellite Image Feature Extraction Using Neural Network Technique. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_13
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DOI: https://doi.org/10.1007/978-81-322-0740-5_13
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0739-9
Online ISBN: 978-81-322-0740-5
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