Speckle Filtering of GF-3 Polarimetric SAR Data with Joint Restriction Principle

Abstract

Polarimetric SAR (PolSAR) scattering characteristics of imagery are always obtained from the second order moments estimation of multi-polarization data, that is, the estimation of covariance or coherency matrices. Due to the extra-paths that signal reflected from separate scatterers within the resolution cell has to travel, speckle noise always exists in SAR images and has a severe impact on the scattering performance, especially on single look complex images. In order to achieve high accuracy in estimating covariance or coherency matrices, three aspects are taken into consideration: (1) the edges and texture of the scene are distinct after speckle filtering; (2) the statistical characteristic should be similar to the object pixel; and (3) the polarimetric scattering signature should be preserved, in addition to speckle reduction. In this paper, a joint restriction principle is proposed to meet the requirement. Three different restriction principles are introduced to the processing of speckle filtering. First, a new template, which is more suitable for the point or line targets, is designed to ensure the morphological consistency. Then, the extent sigma filter is used to restrict the pixels in the template aforementioned to have an identical statistic characteristic. At last, a polarimetric similarity factor is applied to the same pixels above, to guarantee the similar polarimetric features amongst the optional pixels. This processing procedure is named as speckle filtering with joint restriction principle and the approach is applied to GF-3 polarimetric SAR data acquired in San Francisco, CA, USA. Its effectiveness of keeping the image sharpness and preserving the scattering mechanism as well as speckle reduction is validated by the comparison with boxcar filters and refined Lee filter.

Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis

“…Zhu et al [ 3 ] proposed a method of sidelobe suppression based on the sparsity constraint regularization to reduce sidelobes of GF-3 images in sea areas. In addition, three different restriction principles were proposed as a joint one in [ 4 ] to keep the image sharpness while preserving the scattering mechanism as well as speckle reduction of GF-3 images. Moreover, Liu et al [ 5 ] proposed a new method to discriminate water and building shadows with a single SAR image from the GF-3 satellite.…”

Polarimetric Synthetic Aperture Radar Speckle Filter Based on Joint Similarity Measurement Criterion

“…In this paper, the X-band airborne and C-band spaceborne PolSAR data are used for illustration. At the same time, the Re-Lee filter [9], IDAN filter [10], NLM filter [17] and JRP filter [15] are used to compare with the JSMC filter. Among them, the NLM filter is applied in a 21 × 21 searching window with a 7 × 7 patch, and the other three filters are applied with 7 × 7 pixel filter windows.…”

A comparison of algorithms to suppress noise in GF-3 FSII SAR images

“…The GF-3(GaoFen-3) synthetic aperture radar (SAR) imaging satellite was launched on August 10, 2016. GF-3, which is characterized by full-polarization observation, has been widely used in monitoring and information extraction of regions such as oceans, forests, and urban landscapes, and has been applied in many fields, such as disaster assessment [1][2][3][4][5][6][7] . The process of SAR imaging inevitably generates multiplicative speckle noise [8] , which complicates the post-processing and analysis of SAR images [9][10][11][12][13] .…”