NDLI: Application of fractal parameters for unsupervised classification of SAR images: a simulation based study

Content Provider	ACM Digital Library
Author	Pant, Triloki Singh, Dharmendra Srivastava, Tanuja
Abstract	Classification of any satellite image with unsupervised or supervised technique is still very challenging task. A lot of researchers are working for this but still uncertainties exist to label the different classes. Land cover classification with satellite images is very much dependent on the type of satellite images. Nowadays, Synthetic Aperture Radar (SAR) images are giving very promising results in comparison to optical images. Therefore, in this paper a contextual classification has been performed in an unsupervised way for SAR image. For this purpose, fractal parameters viz. fractal dimension and lacunarity is used. In order to apply the methodology, first of all a set of simulated SAR images has been generated and tested for classification and then the proposed methodology is applied on satellite SAR images, i.e., ERS-2 SAR. The classification accuracy for simulated images comes up to 85% whereas for satellite SAR it reaches to 76%.
Starting Page	45
Ending Page	50
Page Count	6
File Format	PDF
ISBN	9781450304085
DOI	10.1145/1963564.1963572
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-12-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sar image Fractal parameters Simulation of sar images Unsupervised classification
Content Type	Text
Resource Type	Article

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Application of fractal parameters for unsupervised classification of SAR images: a simulation based study