Selection of Suitable Window Size for Speckle Reduction and Deblurring using SOFM in Polarimetric SAR Images
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RESEARCH ARTICLE
Selection of Suitable Window Size for Speckle Reduction and Deblurring using SOFM in Polarimetric SAR Images Sanjay Shitole · Shaunak De · Y. S. Rao · B. Krishna Mohan · Anup Das
Received: 26 November 2013 / Accepted: 20 June 2014 © Indian Society of Remote Sensing 2015
Abstract 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
S. Shitole () · S. De · Y. S. Rao · B. Krishna Mohan Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India e-mail: [email protected] S. De e-mail: [email protected] Y. S. Rao e-mail: [email protected] B. Krishna Mohan e-mail: [email protected] A. Das Space Applications Center (ISRO), Department of Space, Government of India, Ahmedabad, 380015, India e-mail: [email protected]
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. Keywords Speckle filter · SOFM · Polarimetric SAR
Introduction Polarimetric SAR (PolSAR) has emerged as a powerful remote sensing tool to extract bio and geophysical properties of the Earth’s surface in all weather conditions. The signals received by the polarimetric sensor for a given location can be in phase or out of phase due to varying degree of surface roughness, which causes brighter or darker pixels in radar images. This interference pattern, a result of signal adding in phase or out of phase is known as fading. This fading is responsible for speckle, which is dominating factor in PolSAR imagery. Speckle is an inherent part which acts as a barrier in the analysis of PolSAR images. It complicates the task of extraction of meaningful information and affects the segmentation and classification of data. Usually, to overcome this, the preprocessing of PolSAR data is done using speckle reduction algorithms. In this study Boxcar, Refined Lee, Lopez, IDAN, Improved Sigma
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