Abstract
Segmentation of SAR image plays an imperative function in analysis of huge amount of satellite data. The good recital of recognition algorithms based on the quality of segmented image. In case of SAR image, it is one of the most complicated and challenging tasks in image processing, and determines the quality of the final results of the analysis. The capability of SAR image is to penetrate cloud cover to predict the weather condition at any particular instant of time. Image data can also be used to classify the land, forest, hills, oceans etc.
In this paper a novel methodology has been carried out to segment a SAR images based on Shannon’s definition of information entropy. Since entropy is a statistical measure of randomness that can be used to characterize the texture of the input image. The basic concept is that the background remains informatively poor, whereas the objects carry relevant information. This method preserves the details, highlights edges, and decreases random noise; all of this is done in one calculation.
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© 2012 Springer-Verlag Berlin Heidelberg
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Samanta, D., Sanyal, G. (2012). Novel Shannon’s Entropy Based Segmentation Technique for SAR Images. In: Venugopal, K.R., Patnaik, L.M. (eds) Wireless Networks and Computational Intelligence. ICIP 2012. Communications in Computer and Information Science, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31686-9_22
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DOI: https://doi.org/10.1007/978-3-642-31686-9_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31685-2
Online ISBN: 978-3-642-31686-9
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