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Change Detection of Water-Body in Synthetic Aperture Radar Images
Sree Sharmila T, Ramar K
Pages - 233 - 242     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 6   Issue - 4    |    Publication Date - August 2012  Table of Contents
Change Detection, Classification, Support Vector Machine
Change detection is the art of quantifying the changes in the Synthetic Aperture Radar (SAR) images that have happened over a period of time. Remote sensing has been the parental technique to perform change detection analysis. This paper empirically investigates the impact of applying the combination of texture features for different classification techniques to separate water body from non-water body. At first, the images are classified using unsupervised Principle Component Analysis (PCA) based K-means clustering for dimension reduction. Then the texture features like Energy, Entropy, Contrast , Inverse Differential Moment , Directional Moment and the Median are extracted using Gray Level Co-occurrence Matrix (GLCM) and these features are utilized in Linear Vector Quantization (LVQ) and Support Vector Machine (SVM) classifiers. This paper aims to apply a combination of the texture features in order to significantly improve the accuracy of detection. The utility of detection analysis, influences management and policy decision making for long-term construction projects by predicting the preventable losses.
CITED BY (2)  
1 Sharmila, T. S., Ramar, K., & Raja, T. S. R. (2014). Impact of applying pre-processing techniques for improving classification accuracy. Signal, Image and Video Processing, 8(1), 149-157.
2 Raja, R. T. Sree Sharmila, K. Ramar & T. Sree.
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Mr. Sree Sharmila T
SSN College of Engineering - India
Dr. Ramar K
- India