Abstract
Abstract. Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.
Highlights
In the interpreting and analyzing filed of Polarimetric Synthetic Aperture Radar (PolSAR) data, feature classification study plays a key vital role(Zhang, 2015)
Instead of combating the speckle noise, we designing a intelligent algorithms for land-use, ground cover classification in SAR images based on the statistical properties of speckle(Lee, 2009)
The complex Wishart distribution agrees reasonably well for measurements over homogeneous backscattering media, but it could not describe the heterogeneous regions in SAR images
Summary
In the interpreting and analyzing filed of Polarimetric Synthetic Aperture Radar (PolSAR) data, feature classification study plays a key vital role(Zhang, 2015). The complex Wishart distribution agrees reasonably well for measurements over homogeneous backscattering media, but it could not describe the heterogeneous regions in SAR images. Frery, 2006 adopts the G0 distribution to feature classification of multilook fully polarimetric SAR data by ICM. In order to describe homogeneous, heterogeneous and extremely heterogeneous regions in the multilook fully polarimetric SAR image effectively, the paper presents a distribution combining G0 distribution and Wishart distribution. To reduce speckle influence for classification result and preserve image resolution, while the original PolSAR image does not have a large enough Equivalent Number of Looks(ENL), we choose multilook polarimetric SAR data process by averaging several independent 1-look coherent matrices and speckle filter with a small window(Lopez-Martinez, 2005).
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