Improved SMB speckle filtering of polarimetric SAR data with synergistic use of orientation angle compensation and spatial majority rule

The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both the decomposition and classification of the scattering properties.In addition,a relatively weak speckle reduction particularly in distributed media was reported in the related literatures.In this work,an improved SMB filtering strategy is proposed considering the aforementioned deficiencies.First,the orientation angle compensation is incorporated into the SMB filtering process to remedy the overestimation of the volume scattering contribution in the Freeman-Durden decomposition.In addition,an algorithm to select the homogenous pixels is developed based on the spatial majority rule for adaptive speckle reduction.We demonstrate the superiority of the proposed methods in terms of scattering property preservation and speckle noise reduction using L-band Pol SAR data sets of San Francisco that were acquired by the NASA/JPL airborne SAR(AIRSAR)system.

Polarimetric whitening filter for POLSAR image based on subspace decomposition

Speckle filtering is an indispensable pre-processing step for applications of polarimetric synthetic aperture radar （POLSAR）, such as terrain classification, target detection, etc. As one of the most typical methods, the polarimetric whitening filter （PWF） can be used to produce a minimum-speckle image by combining the complex elements of the scattering matrix, but polarimetric information is lost after the filtering process. A polarimetric filter based on subspaze decomposition which was proposed by Cu et al specializes in retrieving principle scattering characteristics, but the corresponding mean value of an image after filtering is not kept well. A new filter is proposed for improving the disadvantage based on subspace decomposition. Under the constraint that a weighted combination of the polarimetric SAR images equals to the output of the PWF, the Euclidean distance between an unfiltered parameter vector and a signal space vector is minimized so that noises can be reduced. It is also shown that the proposed method is equivalent to the subspace filter in the case of no constraint. Experimental results with the NASA/JPL airborne polarimetric SAR data demonstrate the effectiveness of the proposed method.

ICA Based Speckle Filtering for Target Extraction in SAR Images Using Adaptive Space Separation

A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.

An assessment of oil spill detection using Sentinel 1 SAR-C images

Identification of an oil spill is additionally essential to evaluate the potential spread and float from the source to the adjacent coastal terrains.In such manner,usage of Synthetic Aperture RADAR(SAR)information for the recognition and checking of oil spills has gotten extensive consideration as of late,because of their wide zone inclusion,day-night and all-weather capabilities.The present examination studies an oil spill occurred in the Al Khafji region by applying Sentinel 1 SAR-C images.Al Khafji is on the borderline between Saudi Arabia and Kuwait in the Persian Gulf and it is detected as an unbiased zone.Al Khafji region can possibly deliver in excess of 7472.403 m³barrels of oil for every day(m³/d).Approaches dependent on multi-sensor satellite images examination have been produced for distinguishing oil spills from referred to common leaks just as oil slick procedures.In this paper,one of these techniques is associated with Sentinel 1 images of a known region of natural oil leakage and of an ongoing oil slick incident in Al Khafji zone.The Synthetic Aperture Radar(SAR)is perceived as the most significant remote sensing apparatus for the ocean and ocean waters oil slick examination,recording,documentation and propagation.Specifically,this paper examines oil spills recognition in the Persian Gulf surveyed by utilizing Sentinel-1(SAR-C)imageries.Results demonstrated the significance of the VV polarization of the Sentinel-1 for recognizing oil-spills just as the diminished utility of the VH polarization in this sole circumstance.