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Selection of Suitable Window Size for Speckle Reduction and Deblurring using SOFM in Polarimetric SAR Images

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Title Selection of Suitable Window Size for Speckle Reduction and Deblurring using SOFM in Polarimetric SAR Images
 
Creator SHITOLE, S
DE, S
RAO, YS
MOHAN, BK
DAS, A
 
Subject CLASSIFICATION
ENTROPY
Speckle filter
SOFM
Polarimetric SAR
 
Description Classification performance of PolSAR data, when used without speckle reduction is insufficient for most applications. Thus, speckle filtering becomes an essential preprocessing step. In this study we evaluate the effectiveness of different popular speckle filters and analyse their effects on the classification accuracy. We have used L-band and C-band fully polarimetric dataset acquired over Mumbai, India. The Wishart supervised classifier algorithm is used for classification of the filtered and unfiltered data. Boxcar, Refined Lee, Lopez, IDAN, Improved Sigma and sequential filters are analysed for the improvement in classification accuracy. Further we also evaluate the effect of window size on classification accuracy in order to be able to select appropriate window for speckle suppression. Boxcar and Refined Lee filters are used to test the effect of speckle filtering on classification with varying moving window size. Boxcar filter is widely used in the SAR application domain owing to it's simplicity. However, the indiscriminate averaging of the Boxcar filter causes a resolution loss in the vicinity of sharp edges and point targets in the image. To overcome this, we have applied Kohonens Self-Organizing Feature Map (SOFM) algorithm to deblurr the image and improve edge and target preservation performance.
 
Publisher SPRINGER
 
Date 2016-01-14T12:41:59Z
2016-01-14T12:41:59Z
2015
 
Type Article
 
Identifier JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 43(4)739-750
0255-660X
0974-3006
http://dx.doi.org/10.1007/s12524-014-0403-7
http://dspace.library.iitb.ac.in/jspui/handle/100/17524
 
Language en